Etikettarkiv: Chalmers

Artificiell intelligens, Distributionsfordon, Fordon, Forskningsprojekt, Funktionell säkerhet, HMI, Informationssäkerhet, Inspiration, Kommunikation, Kooperativa system, Lagstiftning och regelverk, Mjukvara, Mobilitetstjänster, Personbilar, Samhällspåverkan, Simulering, Teknologi, Tester och demonstrationer, Trafiksäkerhet

Guldkorn från svensk forskning

Det här är svenska guldkorn från er läsare. Tack för ert fantastiska jobb.

Eldsjäl: Elektriska delade självkörande fordon i det framtida fossiloberoende transportsystemet
Trafikkontoret i Göteborg, Västtrafik, K2/Malmö Universitet och Trivector har genomfört Drive Sweden-projektet Eldsjäl. Projektet analyserade hur delade självkörande elektriska fordon kan komma att påverka staden och transportsystemet i Göteborgsregionen. Det syftade till att skapa en ökad förståelse för hur elektriska delade självkörande fordon kan påverka och komplettera kollektivtrafiken men också hur transportsystemet i stort påverkas utifrån ett hållbarhetsperspektiv. I projektet utvecklades möjliga framtidsscenarier vilka sedan modellerades i Göteborg Stads nya multimodala VISUM-modell. Från scenarierna erhölls resultat i form av hur trafiken påverkas och parametrar såsom restider, trafikflöden, fordonsflotta och beläggning i fordonen. I projektet genomfördes också digitala djupintervjuer för att få en bättre förståelse för människors inställning, resonemang och behov kring självkörande fordon i staden i allmänt och kring simuleringsresultaten i synnerhet. Mer information om projektet hittar du här eller kontakta Lennart Persson, Trivector, (lennart.persson@trivector.se)

Will leisure trips be more affected than work trips by autonomous technology? Modelling self-driving public transport and cars in Stockholm, Sweden
I studien användes den svenska transportmodellen Sampers för att undersöka vad självkörande fordon skulle innebära för Stockholm, genom en av de första modellstudierna där överflyttning mellan trafikslag ingår. En överflyttning från gång- och cykeltrafik hittades i samtliga scenarier, framförallt till biltrafik men i mindre mån skulle även självkörande teknik för kollektivtrafik innebära att människor åkte kollektivtrafik istället för att gå eller cykla. Nytt var även att vi undersökte geografiska skillnader och kom fram till störst effekter i förorter till Stockholm – med motiveringen att förbättringen av tillgängligheten för kollektivtrafik och bilar i innerstaden relativt sett skulle vara mindre. På samma sätt har de flesta redan bil på landsbygden och avstånden är långa, vilket ger få överflyttningseffekter. Där kan däremot anropsstyrd kollektivtrafik vara ett bra alternativ till bilen. Därutöver, som titeln antyder, undersökte resors olika syften och såg små effekter för transportsystemet för arbetspendling. Istället är det på fritiden som den stora ökningen av tillgänglighet får effekten att människor helt enkelt göra fler resor. Ni kan läsa den vetenskapliga artikeln här. Kontaktperson Erik Almlöf (ealmlof@kth.se)

Frameworks for assessing societal impacts of automated driving technology
I studien gjordes en översikt av de olika ramverk som finns för att utvärdera effekterna av självkörande teknik. Det identifierades 13 tidigare ramverk med ambitionen att täcka mer än ett område (t ex så försvann då ramverk som bara tittade på säkerhetsaspekter) och val av både metod och redovisade områden varierade stort. Det konstaterades att inget av de identifierade ramverken täcker allt, istället har olika ramverk olika styrkor. Därtill användes Trafikverkets metod för samhällsekonomiska bedömningar för att utvärdera en föreslagen autonom busslinje i södra Stockholm och det kunde konstateras att projektet skulle kunna ge stora, och lite oväntade, vinster i form av framförallt bekvämare resor för resenärer, medan t ex olyckor var en relativt marginell faktor. Samtidigt så täcker inte den nuvarande metoden för samhällsekonomiska bedömningar alla aspekter av effekter av självkörande teknik, t ex ökad arbetslöshet, då metoden främst används för infrastrukturåtgärder. Den vetenskapliga artikeln hittas här. Kontaktperson Erik Almlöf (ealmlof@kth.se)

5GCroCo – FIFTH GENERATION CROSS-BORDER CONTROL, a project funded by EU H2020 program
The 5GCroCo project has carried out large-scale connected car trials along two 5G corridors that cross the borders between France-Germany and Luxembourg-Germany. The trials carried out in these corridor areas proved that seamless service continuity on 5G networks can be guaranteed across borders. The service continuity solution implemented in 5GCroCo is achieved through a cross-border (and cross-MNO) handover, which results in an almost imperceptible service interruption time of around 120 ms. The seamless service continuity is important for all of the three use case that were demonstrated: Tele Operated Driving (VW); HD mapping (Volvo Cars); and Anticipated Cooperative Collision Avoidance (PSA and Renault). In today’s mobile networks, the connection breaks and needs to be re-established, which took more than 6 seconds with the devices used in the conducted trials. In all cases the service interruption time is significantly reduced compared to tens of seconds, or even minutes, experienced today when you are crossing a country border. The handover solution implemented in 5GCroCo is thus essential to enable continuous driving experiences between 5G national networks when connected and autonomous vehicles cross from one country to another. In addition to the large-scale trails, tests have been performed at AstaZero, using the 5G network from Ericsson, where a virtual country border was emulated on the rural road test track. 5GCroCo consortium in short: 24 partners from 7 European Countries, Total project budget about 17M€, EC contribution about 13M€, Project duration: 44 Months, 3 CAM key use cases demonstrated. Ni kan läsa mer på länken här. Kontaktperson Mikael Nilsson (mikael.nilsson@volvocars.com)

Assuring Safety for Rapid and Continuous Deployment for autonomous driving (ASSERTED)
Assuring safety of ML-enabled systems like Autonomous Driving (AD) Function in DevOps context is the challenge which will be addressed in ASSERTED. Our research goal is to explore methods and technical solutions for coping better with safety of autonomously driving vehicles for rapid and continuous development and deployment. The project is a collaboration between Volvo Cars, Zenseact, and Chalmers. ASSERTED is funded by Sweden’s Innovation Agency (Diarienummer: 2021-02585), and supported by WASP. https://youtu.be/YRlSpd6NIm8 Contact person Ali Nouri (ali.nouri@volvocars.com)

Digital trafiksäkerhetslösning: en förstudie
En förstudie som ämnar öka förståelsen och möjligheterna för en kostnadseffektiv och robust-över-tid digital trafiksäkerhetslösning som automatiskt varnar för annalkande trafik i obevakade plankorsningar håller nu på att avslutas. Förstudien har koordinerats av RISE tillsammans med företaget Crossing Safety och syftet är att risken för plankorsningsolyckor, samt kostnader för plankorsningsåtgärder, ska kunna reduceras. Ett ”proof-of-concept” utfördes den andra december med en utvecklad mobil-app som varnar för tåg då man befinner sig inom ett visst förutbestämt område från en obevakad plankorsning. Slutrapport publiceras i januari. https://youtu.be/YErx4DjlfyM. Kontaktperson Joakim Rosell (joakim.rosell@ri.se)

AUTOPIA – successful operation in Nordic winter conditions
From April 2021 until January 2022, Ruter and the AUTOPIA partnership trialed a service of AV transport in Ski, Norway. As a feeding shuttle to Ski train station, the pilot project aimed to demonstrate the benefits of a fleet of ride-shared AVs as an integrated part of public transport. Retrofitted Toyota Proaces with Sensible4’s AV technology were used in a publicly open service driving more than 10.000 kilometers through all seasons. Among others, the project resulted in new methodology for site/vehicle matching, experience with key issues of winter operation, and demonstrated that AVs can handle Nordic winter conditions successfully. All learning reports, videos and more can be found here: https://ruter.no/automated-mobility AUTOPIA consisted of the Nordic partners Ruter, Holo, Norwegian Public Road Administration, Viken municipality, TØI and Sensible4 as well as Toyota Motor Europe. Several others were involved in the project, including Edeva from Sweden. eirik.mero@ruter.no Eirik Mero

Augmented CCAM
Augmented CCAM (https://www.augmentedccam.com/) är ett HEU-projekt (https://www.ccam.eu/projects/augmented-ccam/) som syftar till att förstå, harmonisera och utvärdera olika lösningar i den fysiska och digitala infrastrukturen (så kallade PDI-koncept – Physical and Digital Infrastructure) för att förenkla och förbättra storskaligt införande av självkörande fordon. Det kan t.ex. handla om hjälp för att detektera oskyddade trafikanter eller vägarbetspersonal, interaktion med utryckningsfordon eller vävningssituationer. Projektet koordineras av FEHRL och konsortiet består av 26 parter från 13 länder. Från Sverige deltar VTI med körsimulatorförsök som syftar till att undersöka trafikanters interaktioner med något eller några av de framtagna PDI-koncepten samt genomföra trafiksimuleringsexperiment för att skala upp effekter, från studier av enskilda fordon och trafikanter i körsimulator eller digitala tvillingar, till ett trafiksystem med olika andel fordon eller trafikanter som kan utnyttja PDI-koncepten. Johan Olstam (johan.olstam@vti.se)

I4Driving
HEU-Projektet i4Driving (https://i4driving.eu/) syftar till att lägga grunden för en ny standardmetod för utvärdering av säkerhet hos självkörande fordon genom att ta fram en trovärdig och realistisk säkerhetsreferensnivå (hur säkert en mänsklig förare kör i en given situation). Detta dels genom att ta fram ett modulärt och skalbart bibliotek av förarmodeller för simulering och dels genom en metodik för att beakta den stora variationen och osäkerheten i mänskligt förarbeteende i olika situationer. Projektet koordineras av Panteia och konsortiet består av 14 parter samt 3 parter från USA, Australien och Kina. Från Sverige deltar VTI som kommer att bidra med kunskaper kring föraruppmärksamhet, förarmodellering och med körsimulatorförsök i syfte att fånga variation i förarbeteende i olika situationer. VTI kommer också genomföra en variant på Turing-test där tanken är att undersöka om mänskliga förare kan särskilja förarbeteende från den utvecklade förarmodellen från en verklig förare. Kontaktperson Johan Olstam (johan.olstam@vti.se)

GLAD, Godsleverans under den sista milen med självkörande fordon är ett nyligen avslutat projekt som delfinansierats av Trafikverket och utförts av RISE, Clean Motion, Combitech och Aptiv. I projektet undersöktes vilka områden som s.k. Autonomous Delivery Vehicles (ADV) kan användas och vilka utmaningar som måste hanteras vid implementering av sådana fordon för sista-milen leveranser. Man undersökte också interaktioner mellan ADV:er och andra trafikanter, och operatörer som interagerar med ADV:er i terminalmiljö. Flera av studierna utfördes med hjälp av ADV-prototyper som utvecklades under projektet. Prototypen med självkörande funktioner hade ett autonomt transporthanteringssystem (eng. Autonomous Transport Management System, ATMS) som placerades i en molntjänst med kapabilitet för fjärrkontroll. Projektet undersökte även legala aspekter av ADV:er, med fokus på hur de kan klassificeras. Beroende på ADV:ns maxhastighet och lastkapacitet skulle denna typ av fordon kunna klassificeras som antingen 4-hjulig tung motorcykel för godstransport, eller som motorverktyg. Det förstnämnda kan innebära längre väg till marknadsintroduktion p.g.a högre säkerhetskrav. Resultaten från studierna om interaktioner mellan människor och ADV:er visade bl.a att fordonets körbeteende hade en betydande roll i att förmedla fordonets beteende och avsikt att lämna/inte lämna företräde, samt att ljussignaler på fordonet (e-HMI) kan bidra till att lättare förstå fordonets beteende. En studie som gjordes i en simulerad terminalmiljö visade även att kontexten d.v.s terminalscenariot, situationerna och arbetsuppgifterna var viktig för deltagarna att förstå innebörden av fordonets eHMI. Kontaktperson: Mikael Söderman (mikael.soderman@ri.se)

Digital traffic rules for a connected and automated road transport system. Within the framework of Drive Sweden Policy Lab 2021/22, ways towards a future system for digital traffic rules were identified. Sweden has, from an international perspective, come a long way but there are challenges that can only be solved with a common approach. The project gathered relevant actors to understand how the conditions for change look like, as well as how a change would be received by all relevant actors. Actors ranging from those who issue local traffic rules to those who benefit from the information being presented in a machine-readable format (e.g. navigation service providers, vehicle manufacturers, road users etc.). Reliable information is needed already today for various applications and supporting IT systems and will become increasingly important with a connected and automated road transport system. The project Drive Sweden Policy Lab 2021/22 is funded through the strategic innovation program Drive Sweden by Vinnova, Formas and the Swedish Energy Agency. Join our final digital event (in Swedish) and register via Drive Sweden. Contact persons Cilli Sobiech (cilli.sobiech@ri.se) & Jenny Lundahl (jenny.lundahl@ri.se).

Independent assessment in trials with automated vehicles. The Swedish Transport Agency’s regulations and general advice on trials with autonomous vehicles have recently been amended (TSFS 2021:4, last amended by TSFS 2022:82). If the application concerns trials where technical systems are used to a large extent to ensure road safety, the risk assessment in the application should be supplemented with a statement from an independent assessor who examines that the system can ensure road safety. However, there is no further guidance on when an assessment is needed and what it should cover. RISE is gathering relevant vehicle manufacturers, vehicle operators, assessors, and authorities to clarify and harmonize what an independent assessment of road safety should cover, how the new general advice can be applied in practice and what experiences we can build upon for independent assessment and application processes from other countries and transport areas. If you are interested in participating contact Cilli Sobiech at RISE (cilli.sobiech@ri.se) & Jenny Lundahl (jenny.lundahl@ri.se).

Co-opetitive systems of systems for mobility.
In the recently finished research projects Maus and Orm, foundational aspects of co-opetitive systems of systems for future mobility systems have been explored. A co-opetitive system of systems consists of several independently managed and operated constituent systems that are both collaborating in constellations that solve user needs and competing for business. The projects have developed research results in architecture and design, value network flow analysis, governance, decision-making, and policy analysis. The projects are joint work between RISE, AFRY, Volvo Cars, and (for Orm) Trollhättans stad, and have received funding from Vinnova. More information, including one introductory and one visionary movie about the results, can be found at http://www.sos-4-mobility.se/ or by contacting Pontus Svenson (pontus.svenson@ri.se).

Skara Skyddsängel – Infrastrukturtjänster on-demand för säkrare, tryggare och bekvämare aktiv mobilitet
För säkert cyklande i mörka nordiska miljöer krävs ljus. Forskningsprojektet Skara skyddsängel arbetar för att utveckla och testa autonoma drönare som ett alternativt sätt att lysa upp mörka cykelvägar i Skara kommun. Projektet koordineras av RISE med partner Högskolan i Skövde, Jönköpings Universitet och Skara Kommun.  Det övergripande syftet med projektet är att belysa såväl cykelvägar som möjligheter för människor att välja ett hälsosamt, hållbart och kostnadseffektivt resande. Som en del av projektet har fokusgrupp studier utfört i juni med VR och pilotförsök hållits i november på de utvalda cykelvägarna. Tillsammans med testet har intervjuer gjorts för att undersöka människors nuvarande resvanor och förstå mer om känslan av säkerhet i relation till bland annat mörker och om drönarbelysningen kan bidra till att underlätta hållbart resande. I början av 2023 genomför projektet ytterligare en pilotstudie med utökat testmöjligheter. De som bor i Skara som är intresserad är välkomna att anmäla med länken https://forms.office.com/r/eNjgYcfF7M. Ni kan läsa mer om projektet här. Kontaktperson: Lei Chen (lei.chen@ri.se)

DiG Drönarleverans i Glesbygd
Leverans av paket och gods på svensk landsbygd är utmanande med längre leveranstid och transportutsläpp, särskilt i skärgårdsområden där vattentransporter behövs. Klimatförändringarna är en akut fråga som kräver att vi gör allt för att hitta motlösningar, samtidigt driver näthandeln behovet av logistik till en ny tidshöjd. DiG är ett Vinnova-finansierad projekt med syftet att undersöka det senaste inom drönarleverans med anpassningar till svenska landsbygdsegenskaper för att minska utsläppen, öka servicejämlikheten och tillgänglighet. Projektet koordineras av RISE med samarbeten mellan Aerit – den svenska drönarleverans startuppen, ICAx – innovationsgruppen på ICA Gruppen och Norrtälje kommun, med stöd från ICA Nära Gräddö och Öbutiken i Tjocke. Genom året har projektet utvecklat och testat autonoma drönarleveranssystem och integrerat med ICA Pronto appen. Nu i december pågår pilot i Norrtälje och de utvalda kunderna kommer kunna beställa vissa varor med drönare som ett leverans alternativ. Ni kan läsa mer om projektet här. Kontaktperson: Lei Chen (lei.chen@ri.se)

Artificiell intelligens, Bussar, Distributionsfordon, Forskningsprojekt, Infrastruktur, Kartor, Kommunikation, Kooperativa system, Lagstiftning och regelverk, Lastbilar, Mjukvara, Övriga fordon, Personbilar, Positionering, Samverkan människa - maskin, Simulering, Sjöfart, Tester och demonstrationer, Trafiksäkerhet

Guldkorn från svensk forskning 2021

Det här är svenska guldkorn ifrån er läsare. Stort tack för alla bidrag, och tack för ert fantastiska jobb.

PhD thesis: Decision-Making in Autonomous Driving using Reinforcement Learning.
This thesis explores different techniques based on reinforcement learning (RL) for creating a generally applicable decision-making agent for autonomous driving. One highlight is the introduction of methods that can estimate how confident the trained agent is in its decisions, which for example is important if the agent is exposed to situations outside of the training distribution. Another contribution is a method for combining planning and RL, which both improves the quality of the decisions and reduces the required amount of training samples. The full text is available here. This project was supported by Volvo Group, Chalmers, Wallenberg AI, Autonomous Systems and Software Program (WASP), Vinnova FFI, and AI Sweden. For more information, contact Carl-Johan Hoel (carl-johan.hoel@chalmers.se).

L3Pilot – Piloting Automated Driving on European Roads
The L3Pilot project (https://l3pilot.eu/) is the largest EU project on automation so far and ended in October 2021. In this project, Chalmers and Volvo Cars investigated human collaboration with automated vehicles. The Wizard of Oz approach was used both on test track and on public roads to simulate an automated driving feature that did not require drivers to supervise the system. However, the drivers occasionally had to resume manual driving in response to take-over requests. More information about the participants and the publications from this project can be found here. For more information, contact Linda Pipkorn (linda.pipkorn@chalmers.se)

Long-term demonstration of autonomous shuttle fleets in Gothenburg will run between spring 2022 and 2023 as part of the H2020 project SHOW – SHared automation Operating models for Worldwide adoption (https://show-project.eu/). Main contribution of the real-life urban demonstration is the integration of fleets of automated vehicles into public transport, to advance sustainable urban mobility, combined with evaluations of technical solutions, business models, user acceptance and scenarios for impact assessment. The project aims to be the biggest and most holistic initiative ever piloting automated vehicles in urban environments. Real-life urban demonstrations will take place in 20 cities across Europe, such as in Madrid, Turin, Salzburg, Rouen, and Linköping. SHOW gathers a strong partnership including 69 partners from 13 EU-countries and fosters international cooperation. The demonstration in Gothenburg will take place at Campus Johanneberg/Chalmers University of Technology with partners Keolis, Ericsson and RISE. The project has received funding from the European Union’s Horizon 2020 research and innovation programme. For more information contact Cilli Sobiech (cilli.sobiech@ri.se).

Demonstrating remote controlled trucks at Lindholmen/Gothenburg. Within the project SCAT – Safety Case for Autonomous Trucks we will demonstrate goods transport without a safety host onboard and with higher velocity in a mixed traffic environment at Lindholmen (https://www.ri.se/en/what-we-do/projects/safety-case-for-autonomous-trucks). The demonstration will take place in spring 2022. The project started in autumn 2020 with partners RISE, Ericsson, AstaZero, Telia and Einride. The consortium explores together how to safely handle remote access and control from a technical safety perspective and from a policy perspective to support future commercialisation of automated vehicles. We consider the gaps and challenges related to the safety of automated trucks, the digital infrastructure, the policy framework in different markets and their behavioural implications. The approach includes the legal/policy framework in Sweden, as well as France and the US exemplarily. The project is funded through the strategic innovation program Drive Sweden by Vinnova, Formas and the Swedish Energy Agency. For more information contact Cilli Sobiech (cilli.sobiech@ri.se).

Digital traffic rules for a connected and automated road transport system. In the framework of Drive Sweden Policy Lab 2021/22, one case study is identifying ways towards a future system for digital traffic rules (https://www.drivesweden.net/projekt-3/drive-sweden-policy-lab). We raise issues concerning the development of traffic regulations in Sweden through dialogue with a wide range of actors. The purpose is to investigate what is needed to create conditions for a future system with traffic rules that are geographically unambiguous and can be read by machines. Reliable information is needed already today for various applications and supporting IT systems and will become increasingly important with a connected and automated road transport system. We use policy labs as a method to find a possible solution, for example through the development of the regulations that govern how traffic regulations are decided and announced. A development of processes and routines for production, management and exchange of traffic rule data would reduce the risk of deviations that we see today. The project can contribute by looking at challenges, opportunities and alternative solutions linked to the regulations. Drive Sweden Policy Lab is a platform for collaborative policy development enabling smart mobility solutions. The platform gathers governmental agencies, municipalities, multinational corporations, start-ups and research to solve bottlenecks for innovative projects. The project Drive Sweden Policy Lab 2021/22 is funded through the strategic innovation program Drive Sweden by Vinnova, Formas and the Swedish Energy Agency. For more information contact Cilli Sobiech (cilli.sobiech@ri.se).

External interaction principles for creating trust in heavy automated vehicles. To become widely used on public roads, future automated vehicles (AVs) will need to be trusted and gain societal acceptance – something that will be greatly affected by their ability to safely, efficiently and seamlessly interact with other road users in the traffic system. This project investigates if there will be new communication needs when heavy AVs are introduced in traffic. More specifically, the project is investigating how trust and acceptance of heavy AVs can be created and maintained via External Human-Machine-Interfaces (eHMI). Currently, the project has conducted a series of studies including a virtual reality simulator study, and two Wizard of Oz studies on a test track. These studies have been focused on interaction between heavy AV’s and pedestrians. Our next goal is to investigate interaction between heavy AV’s and passenger car drivers using a driving simulator. The project is supporting an institute PhD candidate, and has also hosted two master thesis projects together with Umeå University: Designing eHMI for trucks: How to convey the truck’s automated driving mode to pedestrians and Communicating the stopping intent of an autonomous truck: The interplay between content size, timing and truck speed. This project is financed by Fordonsstrategisk Forskning och Innovation (FFI), associated to SAFER and led by Scania with RISE and Halmstad University as partners. For more information contact Yanqing Zhang (yanqing.zhang@scania.com)

Policy Lab Smarta Fartyg. Projektet undersöker hur den pågående digitaliseringen inom svensk sjöfart rimmar med dagens regelverk. Analysen görs utifrån tre konkreta fall. Två av fallen berör hur autonoma funktioner på ett godtagbart säkert sätt kan ta över människans ansvar ombord utifrån konstruktion och användningsområde. Till skillnad från fordon finns det ingen försöksförordning för autonoma fartyg så arbetet utgår från de regler och undantag som etablerats under en epok när befälhavaren alltid var ombord. I det tredje fallet samverkar två myndigheter kring hur en förändring av dagens lotsplikt kan påverkas av nationella behov och förutsättningar samtidigt som det kommer nya internationella regler. Parter i projektet är Transportstyrelsen, Sjöfartsverket, Saab Kockums, ABB, Färjerederiet och RISE. Projektet finansieras av Trafikverket. För mer information, kontakta projektledare Susanne Stenberg (susanne.stenberg@ri.se) eller Håkan Burden (hakan.burden@ri.se)

Precog: Kravhantering för säkra maskininlärningsbaserade perceptionssystem för autonom mobilitet. Självkörande fordon kräver tillförlitliga perceptionssystem. Framgångsrika perceptionssystem förlitar sig på maskininlärning. Maskininlärning bygger på träningsdata av hög kvalitet. Vad innebär detta för fordonens perceptionssystem? Hur kan vi specificera förväntningarna på träningsdatan? Vad innebär kvalitetssäkring på data-nivån? Hur påverkas fordonets funktionssäkerhet på systemnivån? Den nystartade förstudien Precog genomförs av RISE, Göteborgs universitet, Annotell och Zenseact med stöd från Vinnova. Projektet kommer att skapa samsyn för krav på maskininlärningsbaserade perceptionssystem för fordon. Precog ska utreda kedjan 1) annoteringsnoggrannhet för träningsdata, 2) maskinlärningsmodellernas precision, 3) perceptionssystemens korrekthet och 4) funktionssäkerhet. Förstudien kommer att organisera en serie workshops med nyckelspelare inom svensk fordonsindustri. Vidare kommer dessa workshops att kompletteras med djupintervjuer och litteraturstudier. Efter syntes av projektresultaten kommer vi att arrangera en öppen workshop för att delge våra slutsatser under våren 2022. För mer information kan ni kontakta Markus Borg (markus.borg@ri.se)

Motion-Planning approach for autonomous bus driving. A collaboration between Scania and KTH Royal Institute of Technology resulted in the development of a novel Motion-Planning approach for autonomous bus driving. The results of this collaboration have been recently presented in the IEEE Vehicular Technology Magazine (https://ieeexplore.ieee.org/document/9470918). The article presents a motion-planning framework that leverages expert bus driver behavior, increasing the safety and maneuverability of autonomous buses. To deploy autonomous driving technologies in urban public transport, many challenges related to self-driving buses still need to be addressed. Unlike passenger cars, buses have long and wide dimensions and a distinct chassis configuration, which significantly challenges their maneuverability. To deal with the bus special dimensions, the authors introduce a novel optimization objective that centers the whole bus body as its travels along a road. Furthermore, the authors present a new environment classification scheme that enables self-driving buses to take advantage of the elevated overhangs, to increase maneuverability. Finally, a novel collision checking method is presented that explicitly considers a bus’s front wheels and how they can protrude from beneath the chassis when maneuvering near stops. The benefits of the proposed solution are presented through exp8eriments using an autonomous bus in real road scenarios. The work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. For more information contact Rui Oliveira (rui.oliveira@scania.com) from the KTH Royal Institute of Technology.

Industrial PhD project: Machine Learning to Enhance AI Planning for Intelligent Autonomous Transport Systems. Scania has developed an Offboard system by which its autonomous vehicles can be controlled and managed to perform their operations. This Offboard system can allow an automated planning and scheduling system (a.k.a. AI Planner) to create missions (plan) and dispatch them to the autonomous vehicles. Scania is now researching how to improve AI planning methods for fleets of autonomous vehicles using Machine Learning (ML) techniques. Learning algorithms will support AI planners in order to save human effort leading to good quality plans in less time, thus overcoming the challenge of depending upon the fleet transport managers experience. The PhD project’s outcome is expected to help Scania’s Offboard ATS to improve the plan quality and enable the system to scale up so that it could deal with the future challenges as autonomous vehicles will be taking over in many areas that are of immediate interest to Scania. The project, partly founded by the Swedish Foundation for Strategic Research (SSF), started in April 2020 and it will last 4 years, leading to a PhD degree from Örebro University. For more information contact the Industrial PhD student Simona Gugliermo (simona.gugliermo@scania.com), the industrial supervisor Christos Koniaris (Christos.koniaris@scania.com)  or the academic supervisor Federico Pecora (federico.pecora@oru.se)

Thesis on Cyber Resilient Vehicles. Cyber security focuses on detecting and preventing attacks whereas resilience concentrates on maintaining the vehicle’s intended operation in the presence of faults and attacks, which may even require the vehicle to disable some functionality to protect the passengers in and around the car. This becomes more important when higher levels of autonomy are introduced. In this thesis, we provide methods that aid practitioners in identifying and selecting the necessary and appropriate security and resilience techniques during the design of an automotive system. Additionally, this thesis also proposes three techniques to secure them, namely a mechanism to secure the internal communication, a model to assess a vehicle’s behaviour and reliability when it is driving in traffic, and a framework to detect attacks and anomalies in a vehicle fleet. This thesis was partially supported by the VINNOVA FFI projects HoliSec, and CyReV Phase 1 & 2. For more information contact Thomas Rosenstatter (thomas.rosenstatter@ri.se).

Enhanced ADAS – nästa generations ADAS. Advanced Driver Assistance Systems (ADAS) have the potential to improve traffic safety and efficiency. However, there are challenges with these systems in terms of their limited situation awareness and insufficient driver-vehicle interaction capabilities. If not addressed, these could lead to poor driver experience and decreased use of these systems. This project is led by RISE together with Aptiv and Smart Eye as partners. The aim of this project is to explore how safety, efficiency and drivers’ experience, acceptance and trust can be enhanced by enriching the situation awareness of existing ADAS with real-time information from a) digital road maps, b) driver monitoring, and c) by incorporating dynamic driver-vehicle interaction strategies. The project aims to include two iterations of prototypes with testing of each one on public roads or test track. The first iteration of prototypes has been evaluated and was completed now in december together with expert participants that work in the field of automotive technology. We have received valueable feedback for initiating the second iteration where we aim to develop ADAS functionality together with an intelligent vehicle-driver interface that derives information from internal and external vehicle sensors, as well as digital road maps. This project is financed by Fordonsstrategisk Forskning och Innovation (FFI). For more information contact Niklas Strand (Niklas.strand@ri.se)

The focus of automation in the Project I.hamn. Sweden’s ports are facing a major challenge to function as a transport node in the transformation to a more sustainable transport system that is expressed through the UN’s goals for sustainable development and the strategy for transferring freight transport from land to sea and rail. This means a higher pressure on infrastructure and resources, which places demands on new capabilities in the execution of the port’s operations. Ports need to be more efficient, enable sustainable transport and become a natural node in the integrated transport system. The project I.Hamn (https://www.ri.se/sv/vad-vi-gor/projekt/ihamn) gathers a continuous expanding cluster of today 22 Swedish small and medium sized ports allowing them to join forces to lower thresholds in adopting solution associated to digitalisation, automation, and electrification. The project also involves system and infrastructure suppliers, and other port stakeholder, such as shipping lines, authorities and industry associations. During 2020/2021 the vision of the future port has been co-developed together with involved ports and its stakeholders, through workshops and interviews. Based on the vision, a number of demonstrators are planned for in the areas of electrification, digitalisation, and automation. The demonstrators aim to identify potential and future solutions, based on the capabilities required to realize the vision of the sustainable port. Examples within the area of automation that are exploited are auto-mooring, automatic loading operations, autonomous transports in the port area and automatic hinterland entry and exits to the port. I.hamn is a three-year demonstration project funded by the Swedish Transport Administration within the framework of the Lighthouse industry program for sustainable shipping and coordinated by RISE together with Chalmers and GU. For more information contact Sandra Haraldson (sandra.haraldson@ri.se)

Forskningsprojekt, Okategoriserade

EU projektet L3Pilot avslutas

Det europeiska projektet L3Pilot där man testat fordon med automationsnivå (enligt SAE-skalan) 3 och 4 på allmänna vägar i Europa har avslutats med presentationer och demonstrationer på ITS World Congress 2021 i Hamburg [1].

Projektet som pågått i fyra år har letts av Volkswagen AG och finansierats av den Europeiska Kommisionen med en budget på över 46 miljoner euro, och med totalt 34 projektpartners. Några svenska aktörer var också med i projektet: Volvo Cars, Chalmers, och Veoneer.

I projektet har man totalt utrustat 70 fordon med automationsteknologi och kört 400 000 km på motorväg varav 200 000 km i automatiserat läge, och 24 000 km i stadstrafik i automatiserad läge. Automatiserat läge i det här fallet innebär longitudinell och lateral styrning utan händerna på ratten, men med överlämningar av kontroll mellan fordon och förare. Man har också kört utan överlämning av kontroll i vissa begränsade scenarion exempelvis på parkeringsområden.

Totalt har man utfört olika studier med 600 deltagare i projektet, och de generella resultaten ifrån projektet är att ökad säkerhet är den största fördelen med SAE automationsnivå 3. Man har också insett att fördelen av den här teknologin på samhällsnivå överväger kostnaden för att installera teknologin.

Källa

[1] L3Pilot Press Release. 2021-10-11 Länk

Okategoriserade

Chalmers dataset för mjukvaruutvärdering

Chalmers tekniska högskola har med hjälp ifrån Göteborgs universitet, RISE och sjöfartsverket utvecklat ett dataset vid namn Reeds som går att nyttja för utvärdering av datorseendealgoritmer [1].

Man har spelat in data med sensorutrustning som monterats på en båt där man inkluderat kameror, laser, radar, rörelsesensorer och positioneringssystem. Aktörer kan ladda upp sin datorseendemjukvara till Reeds molntjänst där de sedan kan utvärdera sin mjukvara och jämföra sina resultat med andra aktörer med hjälp av Reeds datasetet.

Målet med tjänsten är att sätta en standard för utveckling och utvärdering av autonoma system, framförallt datorseende som är en viktig del i det hela.

Källa

[1] Spencer, B., ITS International. Swedish AV dataset makes waves. 2021-09-22 Länk

Forskningsprojekt

Fördelar med förarstöd i bussar

Inom Drive Swedens projekt KRABAT har Volvo bussar tillsammans med Chalmers genomfört en studie av hur chaufförerna upplever ett förarstödsystem som tar över inkörningen till hållplatserna [1]. Systemet reglerar bussens hastighet och styrning för att uppnå en komfortabel inbromsning samt att dörrarna hamnar i rätt position i relation till hållplatsläget.

Chaufförernas upplevelse av systemet har utvärderats av forskare på Chalmers, både genom att filma deras beteende medan systemet var aktivt samt genom intervjuer och enkäter. Överlag var chaufförerna positiva och upplevde att systemet avlastade dem då de kunde fokusera på omgivande trafik och passagerarnas säkerhet. Det finns också indikationer på att ergonomin förbättrades med mindre spänningar i axlar och nacke.

Egen kommentar

Projektet är ett intressant exempel på att smarta funktioner och fordons autonomi inte nödvändigtvis innebär en risk för yrkeschaufförer. Förbättrad ergonomi och mindre kognitiv belastning är faktorer som direkt påverkar arbetsmiljön positivt. Det hade varit intressant att se hur mycket av förarstödssystemet kan återutnyttja existerande sensorer och mjukvara, eller i vilken utsträckning funktionen driver nya kostnader, för att relatera dessa mot vinster såsom lägre sjukfrånvaro och mindre skav på fordon.

Källa

[1] Volvo. BUSSFÖRARE SER MÅNGA FÖRDELAR MED AUTONOMT FÖRARSTÖD. 2021-09-07 Länk

Okategoriserade

Guldkorn från svensk forskning 2020

Trust in What? Exploring the Interdependency between an Automated Vehicle’s Driving Style and Traffic Situations. As the progression from partial to fully autonomous vehicles (AVs) accelerates, the driver’s role will eventually change from that of active operator to that of passenger. It is argued that this change will lead to improved traffic safety, as well as increased comfort. However, to be able to reap the benefits, drivers must first trust the AV. Research into automation has shown that trust is an important prerequisite to using automation systems, since it plays an important role in creating user acceptance and in generating a positive user experience. Moreover, for the purposes of safe AV operation, it is important that the user’s trust in the automation is appropriate to the actual capabilities of the system. One important aspect that can build user trust is to conveyvehicle capability, something which is commonly communicated via displays located in the cockpit of the vehicle. However, it has also been shown that parameters such as lateral steering also provide the driver with an understanding of the vehicle’s capability. Therefore, driving styles, or how the act of driving an AV should be conducted, may affect a user’s trust. However, little research has been conducted on the impact of driving styles in AVs in everyday traffic situations; that is, situations often encountered in a day-to-day driving context, such as stopping for a pedestrian at a zebra crossing or overtaking a moving vehicle. An experimental study with 18 participants was conducted on a realistic test course using a Wizard of Oz approach. The experiment included seven everyday traffic situations that the participants’ experienced with two different driving styles, Defensive and Aggressive driving style. The results show that characteristics of everyday traffic situations have an effect on the users trust in automated vehicles (AVs). Primarily due to perceived risks (for oneself and others), task difficulties and how the AV conforms to the user’s expectation regarding how the AV should operate in everyday traffic situations. Furthermore, the results also show that there are are interdependencies between situational aspects and how the AV driving behaviour conducts actions. Thus, the AV driving behaviour needs to be designed to operate differently depending on the traffic situation, to enable the user to create an appropriate level of trust, in relation to the actual performance of the AV. Finally, trust results from the information provided by the AV’s behaviour, what it explicitly communicates via displays, and how these factors relate to the driving context. Thus, a systems approach is necessary, in which the interaction between user and automation is key, but without neglecting the equally important contextual aspects. This study was funded by Vinnova, Sweden’s Innovations Agency, under grant number 2014-01411. The study was able to use the facilities and expertise of the full-scale test environment AstaZero through the open research grant, application number A-0025. Here you can find full paper, and for more information contact Fredrick Ekman at Chalmers (fredrick.ekman@chalmers.se) or read his licentiate thesis titled Designing for Appropriate Trust in Automated Vehicles that was publicly presented earlier this year. 

The Day 1 C-ITS Application Green Light Optimal Speed Advisory. Leveraging the growing communication capabilities between vehicles, infrastructure and other road users, applications under the C-ITS umbrella are expected to improve road safety, traffic efficiency and comfort of driving by helping the driver take decisions and adapt to the traffic situation. The Day 1 set of C-ITS applications, as defined by the C-ROADS platform build on mature technologies and are expected to be deployable and provide benefits in the short term, but what scientific evidence is there on their effectiveness and what gaps in knowledge are there? For the C-ITS Day 1 application Green Light Optimal Speed Advisory (GLOSA), these questions were addressed by a systematic mapping study (to our knowledge, the first such study to be published), conducted as part of the Nordic Way 2 project (co-financed by Connecting Europe Facility, CEF project 2016-EU-TM-0051-S), presented at the European Transport Conference 2019 and published in Transportation Research Procedia in 2020. Among the findings where that while there are many published studies evaluating GLOSA, the absolute majority collect data in simulation, focused mainly on observable effects for the equipped vehicle where fuel consumption and travel time were the most prevalent effects examined. Further, there was great variation in the effects observed (for instance, fuel consumption varied from no evident reduction to approximately 70% reduction between studies) providing little consensus in concluding the effectiveness of the GLOSA application. A possible reason for the big effectiveness variation is a lack of well calibrated models used in the simulations scenarios, especially with regard to driver and fellow road user behaviour and precision of traffic light phase shift prognoses. For more information contact Niklas Mellegård at RISE (niklas.mellegard@ri.se).

Making autonomous drive skilled in extreme situations. During 2020 Sentient finalised the development and testing of the S+ Split-μ Control function, that makes autonomous drive safe in the critical situation of braking in an emergency on split friction roads. Compared to traditional ABS, the braking distance could be reduced by up to 37% while maintaining stability. The function is available also for use in manually driven cars to aid the driver perform like expert drivers would in a split-μ situation. Watch this demonstration from the Colmis test track outside of Arjeplog. More information about safety functions developed by Sentient is available at the company’s website.

Ljuddesign som ökar tillit och minskar åksjuka i självkörande bilar. Hur kan ljuddesign höja användarupplevelsen i automatiserade fordon? Denna fråga har Volvo Cars utforskat de senaste två åren tillsammans med RISE och Pole Position Production. Projektet Ljudinteraktion i Intelligenta Bilar har tagit fram helt nya typer av gränssnitt där passageraren får information om bilens kommande beteende, samt vad i trafikmiljön som bilen fokuserar på. Signalerna låter bland annat snarlikt bilens naturliga ljud vid acceleration och fartminskning, men spelas någon sekund innan bilen agerar. Projektets studier har visat att signalerna ökar passagerarnas tillit till bilen, samt minskar åksjuka för en majoritet av passagerarna. I projektets avslutade del implementeras en prototyp av ljudgränssnittet i en Volvobil, vilket gör det möjligt att uppleva ljuden i verklig trafikmiljö. Resultat från projektet kommer presenteras vid ett seminarium hos SAFER i slutet av januari. Hör av er till projektledaren Fredrik Hagman på Volvo Cars för mer info (fredrik.hagman@volvocars.com), eller besök projektets hemsida. Projektet finansieras av Fordonsstrategisk Forskning och Innovation (FFI).

DI-PPP public and private partnership platform for quick and effective implementation of digital transport infrastructure: This pre-study is jointly financed by Drive Sweden and Trafikverket to accelerate the implementation of digital infrastructure in Sweden. The project uses the Trafikverket roadmap on connected and automated road transport system extensively to explore the synergies and to support the service development. The project defines the digital transport infrastructure from a system of systems perspective with the identification of key areas, action points, and expected achievements for the year 2021 – 2025. The project calls for both top-down and bottom-up approaches to build infrastructure that on the one hand enables applications and services fulfilling the mobility needs, and on the other hand, is built on an existing infrastructure with incremental advancement. The project calls for the establishment of a public and private stakeholder partnership platform that is long-term, proactive and progressive, with strong engagement and balanced investments among stakeholders to accelerate the infrastructure implementation. The results have been presented at the Drive Sweden thematic area digital infrastructure, and for more details and reports, please contact Lei Chen at RISE (lei.chen@ri.se).

Project CeViSS. Cloud enhanced Vehicle – intelligent Sensor Sharing (CeViSS) is a joint Drive Sweden project that has run from January to December 2020. The project was financed in part by Vinnova / Drive Sweden with partnership including Carmenta, CEVT, Ericsson, Volvo Cars and Veoneer. The primary goal of the project was to extend the previously established AD Aware Traffic Control cloud with functions to study and demonstrate how the central cloud platform can be used to collect and enhance critical traffic information before safely sharing it between automotive actors. The project successfully demonstrated how data registered by a Veoneer vehicle’s sensors, was collected, analyzed and enhanced in real-time on the central cloud level and then shared with the two project OEM partners; CEVT and Volvo Cars. Their connected cars could then take appropriate action and more precisely mitigate the hazard on their road ahead. The project also showed how the Carmenta Central Traffic Cloud could send instructions to the Veoneer and CEVT cars such as a recommended speed inside geofences (to be used by the Adaptive Cruise Control (ACC)) and search requests to look for specific symbols or texts (e.g., license plate numbers). Tests were also done where the Central Traffic Cloud had direct control of on-board cameras to start sending video when the Veoneer’s test vehicle approached an accident scene. Images or live video from the scene have the potential to give 112 operators and first responders a better understanding of the situation and help dispatch the right resources as well as make a more detailed planning of the rescue operation before arrival. A series of workshops was arranged during the project with representatives from two rescue organisations to get their response on the value of the technology. Both KatastrofMedicinskt Centrum (KMC) and SOS Alarm confirmed that when planning a rescue operation as well as when organizing the work at the scene it is important to collect as much information as possible about the accident area. Images or live video transmitted from a recent accident under strict control have the potential to improve rescue operations. As the sharing of sensor data in such a way have possible privacy concerns, the legal aspects was also investigated. The results of the legal study is documented in a separate report, added as an appendix to this document. The main deliverables from the project were live proof-of-concept trials performed at several occasions with final tests successfully completed at AstaZero test track, October 19, 2020. A film documenting these tests and explaining the project results was produced and a presentation held at a webcasted Drive Sweden event on December 1, 2020 concluded the project. The project has based its work on the cloud-based platform that was created in the project ”AD Aware Traffic Control” and further extended in the project ”AD Aware Traffic Control Emergency vehicles” and the following ”AD Aware Traffic Control – Advanced Cooperative Driver Assistance” project. The project used technology in Drive Sweden Innovation Cloud and its results will be integrated in this innovation platform for future use. For more information contact Kristian Jaldemark at Carmenta (Kristian.Jaldemark@carmenta.com).

Digital Twins Are Not Monozygotic – Replicating ADAS Testing Across Simulators. Testing in simulators is an essential component in cost-efficient and effective ADAS development. Without countless hours on virtual test tracks, arguing that an ADAS is safe for use on public roads will be practically impossible. However, how can we interpret issues that are detected in a simulator? Would they generalize to the real-world environment? Would they even generalize to another simulator? In a joint study with the University of Luxembourg, RISE used search-based software testing to identify safety violations of a pedestrian detection system in TASS/Siemens PreScan and ESI Pro-SiVIC. However, when replicating the same scenario in the other simulator, the researchers found that the results often differed substantially. Consequently, the researchers recommend future V&V plans to include multiple simulators to support robust simulation-based testing. Make sure the ADAS works safely in other simulators before hitting the real-world roads! The paper pre-print is available here, for more information contact Markus Borg at RISE (markus.borg@ri.se).

Nordic initiative for transport of passengers and goods by drone (NDI): The Nordic countries are joining forces to drive the development of drone transports for both goods and passengers. The Nordic Drone Initiative (NDI) will pave the way for new sustainable business models. It can be about air-taxis, autonomous courier services or new tourist concepts. NDI is co-financed by Nordic Innovation through their Nordic Smart Mobility and Connectivity program, led by RISE and consists of 16 partners from four Nordic countries including RISE, Katla Aero, Flypulse, Kista Science City, Mainbase, LFV and Region Östergötland from Sweden; VTT, Bell Rock Advisors, Robots Expert, Business Tampere from Finland; NORCE, Nordic Edge, UAS Norway and Drone Nord from Norway; and Gate21 from Denmark. The project reference group includes Norwegian Avinor ANS and Finnish ANS. The project is welcoming partners and will collaborate with NEA – the Nordic Network for Electric Aviation to jointly plan for short- and long-haul transports with electric aircraft. For collaborations, please contact Tor Skoglund at RISE (tor.skoglund@ri.se).

Testing safety of intelligent connected vehicles in open and mixed road environment (ICV-Safe): This project is a bilateral joint effort to identify safety-critical scenarios and to develop risk assessment and mitigation methods for intelligent connected vehicles (ICVs) by taking advantage of the large-scale open connected test environment in Shanghai. The project will conduct iterative case design, data collection, simulation, and open road test. The results will lay a foundation for the safe introduction of ICVs to minimize safety risks. RISE is coordinating the Swedish part with partners including Chalmers University of Technology, Alkit Communications AB, WSP AB, and FellowBot AB. The Chinese part is coordinated by Tongji University with partners including Research Institute of Highway (RIOH) Ministry of Transport, Chang’an University, Guangzhou O.CN International Technology Co., Ltd, Shanghai SongHong Intelligent Automotive Technology Co., Ltd., and Beijing Tusen Weilai Technology Co., Ltd (TuSimple). Through the project, the partners are also working actively with Swedish actors in China outside the project consortium to explore synergies for further research collaborations and innovation. For more details, please contact Lei Chen at RISE (lei.chen@ri.se).

CTS – Heterogeneous project. This project aims to investigate effects of autonomous vehicle in a mixed traffic environment, i.e., the traffic where automated vehicles share roads with different types of manually-driven vehicles. Effects on traffic flow and safety are the main interests of the project. An example of upcoming activities in the project is a driving simulation study, which is planned during January-February 2021. The study aims to investigate whether there is a behavior adaptation among human drivers when they share roads with automated vehicles. This project is funded by VINNOVA, and it is within the scope of CTS (The China Sweden Research Centre for Traffic Safety), which is an on-going collaboration within SAFER’s research program. Partners on the Swedish consortium includes VTI, Chalmers, Volvo Cars, and Volvo Group; and partners on the Chinese consortium are RIOH, Beijing Jingwei HiRain, Tsinghua University, and Tongji University. Link: Heterogeneous Traffic Groups Cooperative Driving Behaviours Research under Mixed Traffic Condition | SAFER – Vehicle and Traffic Safety Centre at Chalmers (saferresearch.com).

Drivers’ ability to engage in a non-driving related task while in automated driving mode in real traffic. Engaging in non-driving related tasks (NDRTs) while driving can be considered distracting and safety detrimental. However, with the introduction of highly automated driving systems that relieve drivers from driving, more NDRTs will be feasible. In fact, many car manufacturers emphasize that one of the main advantages with automated cars is that it “frees up time” for other activities while on the move. This paper investigates how well drivers are able to engage in an NDRT while in automated driving mode (i.e., SAE Level 4) in real traffic, via a Wizard of Oz platform. The NDRT was designed to be visually and cognitively demanding and require manual interaction. The results show that the drivers’ attention to a great extent shifted from the road ahead towards the NDRT. Participants could perform the NDRT equally well as when in an office (e.g. correct answers, time to completion), showing that the performance did not deteriorate when in the automated vehicle. Yet, many participants indicated that they noted and reacted to environmental changes and sudden changes in vehicle motion. Participants were also surprised by their own ability to, with ease, disconnect from driving. The presented study extends previous research by identifying that drivers to a high extent are able to engage in an NDRT while in automated mode in real traffic. This is promising for future of automated cars ability to “free up time” and enable drivers to engage in non-driving related activities. The study was conducted by Volvo Cars and RISE in collaboration between two FFI funded projects: TIC – Trust to Intelligent Cars and HARMONISE – Safe interaction with different levels of automation. A pre-print of the paper is available here, and for more information contact Jonas Andersson at RISE (jonas.andersson@ri.se). 

Remote Driving Operation (REDO) project. Remote driving operation or teleoperated driving can support deployment, operation, and testing of automated vehicles. With advancement in wireless communication technology, this has recently becomes more feasible. In the REDO project, we are looking at different technical and non-technical aspects related to teleoperated driving, which include 1) interaction with remote operator; 2) feedback mode from vehicle to remote operator; 3) system architecture; and 4) laws and regulations. Demonstration is also planned towards the end of the project. This is a 3-year project funded by VINNOVA. The partners in the project are: VTI, CEVT, Einride, Ericsson, Ictech, KTH, NEVS, and Voysys. Link: REmote Driving Operation – REDO | Vinnova. For more information contact Maytheewat Aramrattana at VTI (maytheewat.aramrattana@vti.se).

Human factors in remote operation of heavy vehicles. Currently, most highly automated vehicles still require the presence of a human safety operator in the vehicle, and it is evident that automated driving without human “fallback” might be distant. On the other hand, having a human operator in the vehicle jeopardizes major anticipated benefits of automated driving – productivity. This is especially evident when it comes to heavy automated vehicles. To bridge this gap, stakeholders are exploring teleoperations technology, which enables highly automated vehicles to be remotely operated if necessary. But remote operation comes with its own challenges, both from technical and human behavior perspectives. In this SAFER co-financed prestudy, Scania and RISE have identified potential safety challenges and research gaps related to human behavior in the context of remote operation of heavy automated vehicles. A general view of the human factors related challenges within the remote operation topic can be summarized by highlighting phenomena such as physical and psychological distancing, screen delays, network latency delays, inefficient interface designs, and human operator’s cognitive limitations. These are not exclusive to one single operational level, or application type, and are often interrelated. A larger body of scientific work can be found related to human factors in remote operation in other domains (e.g., robotics, aerial drones, military). Some of the findings from these domains can have value for the automotive domain, however, generally design requirements are not directly transferable between domains as there are domain specific challenges. An overall conclusion from the prestudy is that human factors in remote operation of highly automated road vehicles have been somewhat neglected by industry and research community. By providing an overall conceptualization of remote operation and its complexity, a theoretical framework, a state of the art overview, and a list of gaps and challenges, the expectation is that this pre-study will stimulate more activities in the area. The recently started FFI-project HAVOC is example of such an activity. The pre study was co-financed by SAFER and conducted by Scania and RISE. Link to final report, for more information contact Azra Habibovic at RISE (azra.habibovic@ri.se).

Task Force – Hygiene procedures in test with research persons. Since the rapid outbreak and continued global spread of the Coronavirus Disease (COVID-19) in 2020, aspects of much of our day-to-day life in society has been impacted – our workplaces are no exception. Due to the novelty of COVID-19 to health officials in Sweden and around the world, standardized guidelines on how to safely proceed with business activities that require the sharing of physical spaces and/or equipment between individuals has yet to be established. In anticipation of this pandemic being an ongoing issue, a task force was assembled to help address this gap. The SAFER task force was comprised of transport industry professionals in Sweden that have a role in conducting research and testing that would currently be deemed to place individuals at risk of contracting the virus if one of the involved actors were to be an active carrier of the virus. Therefore, the goal of this task force was to help establish a set of general guidelines to consider when attempting to mitigate the risk of contagion while performing research or testing activities at our respective corporate facilities. Questions related to “How can experiments involving test persons in vehicles, driving simulators, virtual-reality studios, or similar test facilities continue?”, “What safety procedures should we consider to introduce in order to ensure proper hygiene for the individuals involved?”, “Is it required for drivers to wear a face mask?”, and “How do we implement physical distancing provisions pre- and post-experiment interviews?” were addressed. Partners in the Task Force were VTI (coordinator), Volvo Group Trucks Technology, Autoliv, Veoneer, RISE and Scania. The project was co-financed by SAFER. For more information contact Arne Nåbo at VTI (arne.nabo@vti.se). 

Bussar, Distributionsfordon, Inspiration, Övriga fordon, Sjöfart

Guldkorn från svensk forskning

Dessa guldkorn är bidrag från våra läsare – stort tack för det, och för all fantastisk forskning och utveckling som ni gör. Keep up the good work!

iQ-Pilot & iQ-Mobility. These are two recently finished projects co-funded by the Strategic vehicle research and innovation programme (FFI). The focus of the projects was development of new technology to realize flexible, energy-efficient transport solutions in cities. Several proof-of-concept prototypes have been developed and demonstrated, including autonomous buses and a smart coordination system for bus fleets. The research results were presented in a webinar earlier this week. These results are the joint efforts of Scania, Ericsson, INIT, Veoneer, Royal Institute of Technology (KTH) and Örebro University. 

Human interaction with autonomous minibuses. Tom Ziemke’s research group at Linköping University, in collaboration with researchers at VTI, will during the autumn start a new research project on people’s interaction with autonomous minibuses on campus. The research will focus on method development and empirical studies of how pedestrians, bicyclists and car drivers interact with the buses. A two-year postdoc position is available via this link (application deadline: August 5). For more information contact Tom Ziemke (tom.ziemke@liu.se).

GLAD – Goods delivery under the Last mile with Autonomous Driving vehicles. Small autonomous electric delivery vehicles (ADV) are expected to transform transportation of goods under the first and last mile. The advantages are increased transportation and energy effectiveness, but it is also important that these vehicles are safe and accepted in society. The aim of the GLAD project is to develop an initial knowledge base on efficiency, safety and human experience of ADVs for the first and last mile delivery of goods in Sweden, and on how to create a balance between these three aspects from a socio-technical perspective. To achieve this, the project will utilize Zbee vehicles that will be adapted in terms of vehicle design and autonomous vehicle behaviour, human-machine interface, teleoperation and vehicle management. The overall goal is to develop knowledge that accelerate introduction of new efficient goods delivery in our society and contributes to meeting the goals of Agenda 2030. This will be assured also by connecting a licentiate candidate to the project. The project is co-funded by Trafikverket and involves RISE, Halmstad University, Aptiv, Combitech and Clean Motion. It started in June 2020 and will run for ca 2 years. For more information contact azra.habibovic@ri.se.

Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation. Reinforcement learning (RL) can be used to create a tactical decision-making agent for autonomous driving. However, previous approaches only output decisions and do not provide information about the agent’s confidence in the recommended actions. This paper investigates how a Bayesian RL technique, based on an ensemble of neural networks with additional randomized prior functions (RPF), can be used to estimate the uncertainty of decisions in autonomous driving. A method for classifying whether or not an action should be considered safe is also introduced. The performance of the ensemble RPF method is evaluated by training an agent on a highway driving scenario. It is shown that the trained agent can estimate the uncertainty of its decisions and indicate an unacceptable level when the agent faces a situation that is far from the training distribution. Furthermore, within the training distribution, the ensemble RPF agent outperforms a standard Deep Q-Network agent. In this study, the estimated uncertainty is used to choose safe actions in unknown situations. However, the uncertainty information could also be used to identify situations that should be added to the training process. The paper will be presented at the Intelligent Vehicles Symposium (IV) in October 2020, and a preprint is available on arXiv. The code that was used is also available on GitHub For more information, contact Carl-Johan Hoel (carl-johan.hoel@volvo.com) at Volvo Autonomous solutions. This work was partially supported by the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP), funded by Knut and Alice Wallenberg Foundation, and partially by Vinnova FFI.

Autonomous Mapping of Unknown Environments Using a UAV. As part of the research conducted within the project LASH-Fire (Eu-Horizon 2020, No.814975), RISE supervised the work of Chalmers students developing an automatic object search for indoor environments using a flying drone. At the core of this system a reinforcement learning (RL) algorithm was implemented for the drone to navigate, detect obstacles, recognize objects and explore the environment. This machine learning (ML) project marks a starting point for further development towards an autonomous identification and surveillance solution in a wide range of study cases where cargo ships, like the ones studied in LASH-Fire, are an ideal target application. A modularized approach was used targeting research areas such as obstacle avoidance, object detection & recognition, simultaneous localization and mapping, etc. The exploration module was specially challenging and will require further work but the project in general was successful in providing a methodology and tools when using flying drones for indoor environments. The Master’s thesis was conducted by Erik Persson and Filip Heikkilä, and is available via this link. For more information contact boris.duran@ri.se

Projektet ESPLANADE, som började 2017 och avslutades sista mars 2020, handlade om hur man visar att ett automatiserat fordon är säkert. Det finns flera problem som måste hanteras för att man ska kunna göra en komplett säkerhetsargumentation. Projektets resultat inkluderar därför nya metoder för säkerhetsargumentation för en ADS, några av dessa är: 

  • En process för säkerhetsanalys samt designprinciper för interaktionen när en människa överlämnar kontrollen över ett fordon till en ADS eller tvärtom. Processen innehåller existerande metoder som sekvensdiagram, orsak-konsekvensanalys och felträd, men applicerade på människa-maskininteraktion istället för enbart tekniska system
  •  Hur man definierar den operativa designdomänen (ODD) för en ADS utgående från önskade användningsfall, vilket innebär en definition av parametrar inom vilka en ADS-funktion är avsedd att fungera, samt strategier för att säkerställa att fordonet håller sig inom sin ODD.
  • En metod (kallad QRN) för riskanalys och framtagande av säkerhetsmål. Till skillnad från vanliga riskanalysmetoder bygger den inte på analys av specifika situationer utan på definition av acceptabel frekvens av incidenter med olika allvarlig konsekvens, och en mappning av incidenter till olika klasser av konsekvenser. Säkerhetsmålen uttrycks så att man säkert hamnar inom acceptabla frekvenser.
  • Ett ramverk för formell och systematisk hantering av säkerhetskrav med en kombination av åtgärder under utveckling och under drift, bland annat baserat på modeller av osäkerhet.
  • Användning av metoden funktionsanalys för att distribuera beslutsfattande på en ADS-arkitektur samt framtagande av säkerhetskrav.
  • Säkerhetskontrakt och komponentbaserad design för att underlätta kompletthetsbevisning i kravnedbrytning, möjliggöra kontinuerlig produktuppdatering, samt kunna uttrycka säkerhetskrav för sensorsystem som inkluderar kamera, radar mm.

En publik rapport och länkar till de flesta av projektets publikationer finns på projekthemsidan.

Prepare Ships Project. Running for 26 months, the H2020 project “Prepare Ships”, funded by the European Global Navigation Satellite System Agency (GSA), was successfully started in December 2019. The 5 consortium partners, coming from 3 European countries have developed a machine learning based future position prediction for ships in order to avoid ship collisions and close quarter situations as well as reducing environmental impact by more advanced decision making. In a RTK (Real Time Kinematic) software solution, it will both exploiting the distinguished features of Galileo signals as well as combining it with other positioning and sensor technologies. It will use the next generation maritime communication techniques VDES and the new suit of IALA Standards (S100) on sea charts. The innovation developed during the project can make more autonomy of navigation feasible by exchanging future positions and allow eased decision making on ships, suitable to become an international game changer for the future of autonomous shipping. The demonstration and testing will be done onboard three different vessels in the Gothenburg archipelago. The project is coordinated by RISE with partners from across Europe, including SAAB, Lantmäteriet, Telko and Anavs. For more information check out our homepage, join our linkedin group or contact Johannes Hüffmeier at RISE (johannes.huffmeier@ri.se).  

How do you ensure safety of autonomous shipping? Today’s risk assessment methods, application of methods and models used in shipping are usually based on humans being directly in charge of ships, VTS, port controls, etc. and may not be sufficient to reflect and evaluate the complexities and inherent risks of introducing further automation and digitalization in the shipping domain. The introduction of smart ships will create traffic situations between manned and unmanned ships where on one hand decisions and actions are based on algorithms and on the other hand by a human operator where a large part of the decision making. Increasing the level of automation implies that the goal-based standards for shipping need to be based on a risk assessment that reflects the expected roadmaps towards more smart ships and so far, research on autonomous transportation has focused on other parts than the effect of introducing and mixing different levels of automation and only very basic standards have been proposed by classification societies, where DNVs standards [DNV, 2018] have two pages in the appendix on basic set-ups for testing and validation. The main objective of the RFAF project financed by Trafikverket is to analyse how autonomous navigation can be proven to be safe. The aim of the project is to perform a simulator-based risk identification for autonomous shipping traffic. Increasing the level of automation implies that the goal-based standards for shipping need to be based on a risk assessment that reflects the expected roadmaps towards autonomy. Based on two use cases, the routes Fredrikshamn-Göteborg and crossing of the Ljusterö fairway, relevant risks are identified based on ship simulations performed by mariners describing especially nautical challenges for more autonomous shipping resulting in a common risk model. The project lasts from January 2020-December 2022. There are 3 project partners with RISE as coordinator. For more information visit the project website or contact Johannes Hüffmeier (johannes.huffmeier@ri.se).

The SWEA-financed (Energimyndigheten) Data-driven Optimised Energy Efficiency of Ships is a national project involving 7 ship owners, 3 companies from the supply chain and RISE, lasting for 16 months. The data analysis of energy consumption is often complex and there are different driving forces for decisions. However, increased data collection can be unprofitable if you do not have methods to analyze the complex systems. Developments within machine learning provides new opportunities to develop both technically and economically powerful tools energy efficiency. Even today, to some extent, economic driving is applied, for example. eco-driving, however, the effect is in many cases limited as decision-making is more complex than the operator / navigator can see. Also, not always available incentives and motivation of individuals to reduce energy use. However, data collection is increasing both quality review and analysis are not performed to the same extent. Using the results of the project’s data collection and analysis, recommendations can be given about which tools which can be developed in a next step, such as: a) nudging, decision support system or autopilot for ECO driving, b) route optimization based on the ship’s accelerations and motions, and c) decision support based on statistics or real-time analysis of data to identify optimal operation (parameters such as sea state, current, speed, load condition, etc.). The objectives of the project are to: a) Achieve reduced energy use on the project’s vessels by 10–35% both at quay and in sea operations, b) Demonstrate potential with machine learning of operational data, and c) Demonstrate the possibility that better operational data may form the basis for the development of generic energy efficiency tools for smaller vessels in commercial traffic. For any details on the project, reach out to Johannes Hüffmeier (johannes.huffmeier@ri.se).

Photonics Private Public Partnership Roadmaps for EU’s next Framework Program Horizon Europé. The area of photonics for automotive applications is a significant area which includes not only photonics sensors for the EU defined topic Mobility and Safety for automated Road Transport. Photonics also plays a role in the path towards the targets of Zero Emission Road Transport, Clean Energy Transition, and the Industrial Battery Value Chain. The work of defining the Strategic Research Agenda (SRA) in the specific area of Photonics with EU industrial partners, universities and research centers is performed through the EU technology platform ”Photonics21”, which is funded by the EU commission. The current roadmap for Photonics was published in the document: “Europe’s age of light! How photonics will power growth and innovation, Strategic Roadmap 2021–2027” The section on Automotive and Transport can be found in section 3.9. The coordinator of the whole Photonics 21 is done by VDI Technologiezentrum GmbH in Düsseldorf, Link. We believe this is important as there are a lot of EU research money at stake. The current recommendation by the European Parliament for the whole Horizon Europe budget 2021 -2017 is €120 Billion. The research funding will be divided among many topics where Climate, Energy, and Mobility is one of the clusters. There is a large Swedish interest in the cluster and cooperation with industry is one important factor in the program. Most, if not all, of the European automotive industry are usually involved in at least selected programs.

Now, based on feedback from the new European Commission, the board of Photonic21 have decided to reshape the roadmap and as a consequence automotive & transport will henceforth be combined with the topics of climate and energy. Besides merging the different topics in one document, this gives us an opportunity to revise the previous document into something that we believe should support our industry even better, considering that the current document was prepared in 2018 and the present situation the industry is facing. We want to ensure that the guiding document capture the specific needs of the automotive industry. The aim of the work is to define the research topics of the Strategic Research Agenda (SRA) which will define the upcoming calls in the Horizon Europe program. 

We now invite comments on the current chapter and roadmap (provided in the link above). Determined by EU commission schedules this work has to be completed on 4 September, why we need your input no later than 24 August 2020. We ask for specific text suggestions and specific roadmap suggestions (compare with p. 140 in the above mentioned Strategic Roadmap). Please forward your suggestions to Jan-Erik Källhammer at jan-erik.kallhammer@veoneer.com. He acted as chair of the group Automotive and Transport in the current roadmap and now act as co-chair of the new group Climate, Energy, and Mobility together with Dr. Heinz Seyringer of V-Research GmbH in Austria. 

Konferenser

Tips på presentationer

  • Masterstudenter från Chalmers ska presentera sina uppsatser den 11 juni från kl 08:00 till 17:30. Exempel på ämnen är avsiktsigenkänning, simuleringar och prediktion av kroppsskador. Eventet kommer hållas på Zoom. Länk
  • Två licentiatseminarium med koppling till automatiserad körning hålls den 16 juni kl 13:15 respektive den 18 juni kl 09:00. Den första inom ämnet förarbeteenden med förarassistans, och den andra inom ämnet design för tillit i AD. Länk1 Länk2
  • En workshop hålls online i ett samarbete mellan Inside GNSS, Hexagon, Inside Unmanned Systems, Novatel och Spirent den 16 och 17 juni. Ämnet är säkerhet av automatiserade fordon och ISO. Registrering och mer info i Länk
Inspiration, Konferenser

Tips på tidskrift och events

HMI, Samverkan människa - maskin, Tester och demonstrationer

Samband mellan kontext och användning av ADAS

En grupp forskare från Chalmers och Volvo Cars har i en nyligen publicerad studie utforskat hur yttre faktorer som trafikmiljö och väder påverkar användningen av avancerade förarstödsystem (ADAS) [1]. 

Studien är baserad på naturalistisk datainsamling från 132 Volvo-bilar och data har samlats in under sju månader. Utöver data från sensorer har forskarna genomfört kompletterande intervjuer med förarna och därmed kombinerat både kvalitativa och kvantitativa data i sina analyser. Bilarna tillhörde studiedeltagarna och användes av dem för diverse resor i vardagen. 

Resultaten visar på samband mellan förarbeteende, systemprestanda och yttre faktorer. Yttre faktorer påverkar prestandan hos ADAS som i sig påverkar förarnas tilltro till och vilja att använda ADAS på lång sikt. Yttre faktorer påverkar också förarens beslut om aktivering och avaktivering av ADAS. 

Baserat på dessa resultat föreslår forskarna att systemutvecklare och designers behöver ta yttre faktorer i beräkningen under hela utvecklingsprocessen. 

Källor

[1] Orlovska et al., Transportation Research Interdisciplinary Perspectives. Effects of the driving context on the usage of Automated Driver Assistance Systems (ADAS) -Naturalistic Driving Study for ADAS evaluation. 2020-02-07 Länk