Kategoriarkiv: Sjöfart

5G-Routes testar 5G över gränser

I EU-projektet 5G-routes, som koordineras av Ericsson, har fordon-till-allt(V2X)-piloter genomförts på en tävlingsbana i Lettland. Bland annat simulerades uppkoppling över landsgränser [1, 2].

Projektet utför piloter med 5G-uppkopplade fordon över landsgränser mellan Lettland, Estland och Finland. Användarfall som har utforskats på tävlingsbanan är b.la: kolonnkörning (platooning), kollisionsundvikning med utsatta trafikanter (Vulnerable Road Users: VRU), uppkopplad vägunderhållning. Målet är att uppkopplingen ska fungera sömlöst över gränsen inom europa.

Ni kan se en video som beskriver arbetet som görs här.

Källa

[1] Stone, T., Traffic Technology Today. First 5G cross-border V2X tests completed in Latvia. 2022-05-18 Länk

[2] Roper, J., Intertraffic. Cross-border connectivity. Länk

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)

Yara utvecklar autonomt containerfartyg

Yara Birkeland är ett helelektriskt, 80 meter långt containerfartyg som ska avlasta vägtrafiken och frakta konstgödsel mellan Yaras fabrik i Porsgrunn och exporthamnen Brevik i södra Norge, en sträcka på ca 14km [1].

Vi rapporterade om detta projekt 2017 här. Nu är alltså planen att det inom två år ska vara helt autonomt. Fartyget ska kunna lasta på och av, ladda sina batterier och navigera utan mänsklig inblandning. Fartygets sensorer ska kunna upptäcka och förstå objekt i vattnet, som till exempel kajaker, och kunna navigera på ett säkert sätt.

Egen kommentar

Fordonsindustrin har över tid insett att autonom körning på väg är minst sagt utmanande. Det blir intressant att följa hur det går för sjöfarten.

Källor

[1] Klesty, V., Reuters. Yara debuts world’s first autonomous electric container ship. 2021-11-19 Länk

Autonom båt i Amsterdam

I det sista av en serie av tre projekt som pågått under fyra års tid har nu en grupp ifrån Massachusetts Institute of Technology, Artificial Intelligence Laboratory (CSAIL), Senseable City Laboratory och Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute) ifrån Nederländerna pilotat två autonoma båtar i Amsterdams kanaler [1].

Båtarna som de kallar för Roboat är eldrivna och kan transportera upp till fem personer, samla upp skräp, och leverera gods.

Bilder och video finns i Reuters artikel nedan.

Källor

[1] Gordon, R., MIT. One autonomous taxi, please. 2021-10-27 Länk

[2] Sterling, T., Reuters. Self-driving ”Roboats” ready for testing on Amsterdam’s canals. 2021-10-27 Länk

Självkörande färjor och båtar

Trafikverket Färjerederiet satsar på klimatsmarta vägfärjor med en upphandling på fyra primärt eldrivna färjor till Stockholms skärgård [1].

Dessa färjor kommer åka Ljusteröleden och Vaxholmsleden och har förutom elmotorer även diesel- eller HVO-motorer. Dessutom kommer färjorna utföra lossning, åkning och förtöjning autonomt. Färjorna får plats med 60 bilar.

Upphandlingen pågår fram till 19 mars 2021.

I en annan nyhet har Zeabus, ett norskt företag, utvecklat en självkörande och eldriven färja till Trondheim i Norge [2].

Båten kan transportera upp till 12 personer inklusive cyklar, och skall kunna hämta passagerare från ena sidan av kanalen till den andra genom ett knapptryck från respektive sida.

En tredje nyhet på samma tema är att forskarna från MIT presenterat en ny båtprototyp ämnad för användning i Amsterdam [3]. Båten heter Roboat II, är självkörande och är både tyngre och längre än dess föregångare. Den kan därmed transportera passagerare.

Källor

[1] Kullenberg Rothvall, C., Sjöfartstidningen. Färjerederiet upphandlar fyra nya vägfärjor. 2020-10-21 Länk

[2] Cairns, R., CNN. Norway pioneered electric ferries. Now it’s making them self-driving. 2020-10-23 Länk

[3] Gordon, R., MIT News. Autonomous boats could be your next ride. 2020-10-26 Länk

Jordbruksmaskiner, skepp och drönare

Hexagons sensor kit. Hexagon som utvecklar teknologi för jordbruksmaskiner har presenterat en traktor med självkörande teknologi. Traktorn är framtagen som en forskningsplattform, och företaget har redan nyttjat traktorn för att validera sitt nya paket med sensorer och positioneringsenheter. Paketet har utvecklats i samarbete med AutonomouStuff och NovAtel. Länk1 Länk2

Självkörande skepp över atlanten. Ett autonomt skepp vid namn Mayflower ska korsa atlanten under April 2021. Skeppet har en ”AI-kapten” och förlitar sig på radarer, kameror och kommunikation till andra båtar för att navigera sig till sin måldestinationen. Innan resan över Atlanten sker så kommer skeppet testas. Detta kommer att ske under kommande veckor. Länk

Drönarleveranser i North Carolina. Den amerikanska detaljhandelskedjan Walmart meddelar att de ska utföra ett pilotprojekt med fokus på drönarleveranser i North Carolina i USA. Detta görs tillsammans med det israeliska företaget Flytrex som har en lösning för drönarleveranser. Länk

Självkörande bussbåt

I Naganohara i Japan finns det en bussbåt (amfibiebuss) som kommer att omvandlas till självkörande för att öka dess turistattraktivitet [1].

Projektet kommer att inledas i december och projektdeltagarna består av Japan Amphibious Vehicle Association, Saitama Institute of Technology (SIT), ITbook Holdings, ABIT Corporation, och staden Naganohara. Projektet har erhållit 250 miljoner Yen (ca 20 miljoner kronor) som till största del kommer ifrån Nippon Foundation.

På busstemat så kan ni också läsa om ett betalsystem som baseras på ansiktsigenkänning och som testas i självkörande bussar (och metro) i Japan [2]. Här kan ni se hur det går till.

Källor

[1] Boyd, J., IEEE Spectrum. Small Japanese Town to Test First Autonomous Amphibious Bus. 2020-08-27 Länk

[2] Japan Today. Self-driving buses being tested across Japan let you pay with your face. 2020-09-09 Länk

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 EuropéThe 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. 

Autoship

Detta är namnet på ett nytt fyraårigt projekt som bedrivs inom ramen för HORIZON 2020 [1]. Projektet har en budget på över 20 miljoner euro och omfattar flera europeiska parter, däribland Kongsberg och SINTEF från Norge. 

Syftet med projektet är att påskynda utvecklingen av nästa generations autonoma fartyg och att skapa en färdplan för kommersialisering av sådana fartyg inom EU under de kommande fem åren.

Under projektets gång kommer Kongsberg att installera och testa autonom teknik på två fartyg. Dessa kommer att demonstreras för användning vid kustsjöfart och Europas inre vattenvägar.

Källor

[1] World Maritime News. Autonomous Ship Project Bankrolled by EU. 2020-01-22 Länk

Norska autonoma transportfartyg

Norska livsmedelstransportföretaget ASKO planerar frakta gods över Oslofjorden med elektriska autonoma fartyg från 2024 [1].

Fartyget, som ska kunna ta 16 semitrailers, är tänkt att gå mellan Moss och Holmestrand och även lastas och lossas helt fossilfritt. På detta sätt kan man spara 5 000 ton koldioxid årligen.

Projektet stöttas av Enova med 119 MNkr.

Egen kommentar

Bra att man sparar på koldioxidutsläppen men varför måste fartyget vara autonomt?

Källa

[1] 119 Enova-millioner til ASKOs autonome fartøy, Enova 2019-03-16 Länk