Advising Autonomous Cars about the Rules of the Road
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Abstract (arXiv)
This paper describes (R)ules (o)f (T)he (R)oad (A)dvisor, an agent that provides recommended and possible actions to be generated from a set of human-level rules. We describe the architecture and design of RoTRA, both formally and with an example. Specifically, we use RoTRA to formalise and implement the UK "Rules of the Road", and describe how this can be incorporated into autonomous cars such that they can reason internally about obeying the rules of the road. In addition, the possible actions generated are annotated to indicate whether the rules state that the action must be taken or that they only recommend that the action should be taken, as per the UK Highway Code (Rules of The Road). The benefits of utilising this system include being able to adapt to different regulations in different jurisdictions; allowing clear traceability from rules to behaviour, and providing an external automated accountability mechanism that can check whether the rules were obeyed in some given situation. A simulation of an autonomous car shows, via a concrete example, how trust can be built by putting the autonomous vehicle through a number of scenarios which test the car's ability to obey the rules of the road. Autonomous cars that incorporate this system are able to ensure that they are obeying the rules of the road and external (legal or regulatory) bodies can verify that this is the case, without the vehicle or its manufacturer having to expose their source code or make their working transparent, thus allowing greater trust between car companies, jurisdictions, and the general public.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 39 references that are in the map. First posted on arXiv 28 Sep 2022.
Builds on
12 of 39 references.
References · 12
- 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 301
- 2019Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactionscited by 117
- 2015The social dilemma of autonomous vehiclescited by 47
- 2018Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry riskscited by 21
- 2018Incorporating Behavioral Constraints in Online AI Systemscited by 16
- 2020Implementations in Machine Ethics: A Surveycited by 16
- 2020Reinforcement Learning Under Moral Uncertaintycited by 16
- 2020Landscape of Machine Implemented Ethicscited by 13
- 2019Deep Learning and Control Algorithms of Direct Perception for Autonomous Drivingcited by 10
- 2016Formal Verification of Autonomous Vehicle Platooningcited by 4
- 2017A Rational Agent Controlling an Autonomous Vehicle: Implementation and Formal Verificationcited by 2
- 2021Grounding-aware RRT* for Path Planning and Safe Navigation of Marine Crafts in Confined Waterscited by 1
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