Advising Autonomous Cars about the Rules of the Road
- URL: http://arxiv.org/abs/2209.14035v1
- Date: Wed, 28 Sep 2022 12:22:59 GMT
- Title: Advising Autonomous Cars about the Rules of the Road
- Authors: Joe Collenette (The University of Manchester), Louise A. Dennis (The
University of Manchester), Michael Fisher (The University of Manchester)
- Abstract summary: (R)ules (o)f (T)he (R)oad (A)dvisor) provides recommended and possible actions to be generated from a set of human-level rules.
We use RoTRA to formalise and implement the UK "Rules of the Road"
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Driving with Regulation: Interpretable Decision-Making for Autonomous Vehicles with Retrieval-Augmented Reasoning via LLM [11.725133614445093]
This work presents an interpretable decision-making framework for autonomous vehicles.
We develop a Traffic Regulation Retrieval (TRR) Agent based on Retrieval-Augmented Generation (RAG)
Given the semantic complexity of the retrieved rules, we also design a reasoning module powered by a Large Language Model (LLM)
arXiv Detail & Related papers (2024-10-07T05:27:22Z) - Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea [8.017543518311196]
Reinforcement learning (RL) is a promising method to find motion plans for autonomous vehicles.
Our approach accomplishes guaranteed rule-compliance by integrating temporal logic specifications into RL.
In numerical evaluations on critical maritime traffic situations, our agent always complies with the formalized legal rules and never collides.
arXiv Detail & Related papers (2024-02-13T14:59:19Z) - Robust Driving Policy Learning with Guided Meta Reinforcement Learning [49.860391298275616]
We introduce an efficient method to train diverse driving policies for social vehicles as a single meta-policy.
By randomizing the interaction-based reward functions of social vehicles, we can generate diverse objectives and efficiently train the meta-policy.
We propose a training strategy to enhance the robustness of the ego vehicle's driving policy using the environment where social vehicles are controlled by the learned meta-policy.
arXiv Detail & Related papers (2023-07-19T17:42:36Z) - Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle.
In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
arXiv Detail & Related papers (2022-07-05T16:33:20Z) - Quantification of Actual Road User Behavior on the Basis of Given
Traffic Rules [4.731404257629232]
We present an approach to derive the distribution of degrees of rule conformity from human driving data.
We demonstrate our method with the Open Motion dataset and Safety Distance and Speed Limit rules.
arXiv Detail & Related papers (2022-02-07T09:14:53Z) - Smart Automotive Technology Adherence to the Law: (De)Constructing Road
Rules for Autonomous System Development, Verification and Safety [0.7481220126953327]
Driving is an intuitive task that requires skills, constant alertness and vigilance for unexpected events.
Modern motor vehicles now include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task.
The UK Department of Transport's response to the Safe Use of Automated Lane Keeping System consultation proposes that these systems are tested for compliance with relevant traffic rules.
This paper presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow.
arXiv Detail & Related papers (2021-09-07T09:22:15Z) - End-to-End Intersection Handling using Multi-Agent Deep Reinforcement
Learning [63.56464608571663]
Navigating through intersections is one of the main challenging tasks for an autonomous vehicle.
In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided.
We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
arXiv Detail & Related papers (2021-04-28T07:54:40Z) - AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles [76.46575807165729]
We propose AdvSim, an adversarial framework to generate safety-critical scenarios for any LiDAR-based autonomy system.
By simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack.
arXiv Detail & Related papers (2021-01-16T23:23:12Z) - Emergent Road Rules In Multi-Agent Driving Environments [84.82583370858391]
We analyze what ingredients in driving environments cause the emergence of road rules.
We find that two crucial factors are noisy perception and agents' spatial density.
Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving.
arXiv Detail & Related papers (2020-11-21T09:43:50Z) - Intelligent Roundabout Insertion using Deep Reinforcement Learning [68.8204255655161]
We present a maneuver planning module able to negotiate the entering in busy roundabouts.
The proposed module is based on a neural network trained to predict when and how entering the roundabout throughout the whole duration of the maneuver.
arXiv Detail & Related papers (2020-01-03T11:16:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.