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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)