Mind the Gaps: Logical English, Prolog, and Multi-agent Systems for Autonomous Vehicles

• URL: http://arxiv.org/abs/2502.09216v1
• Date: Thu, 13 Feb 2025 11:49:17 GMT
• Title: Mind the Gaps: Logical English, Prolog, and Multi-agent Systems for Autonomous Vehicles
• Authors: Galileo Sartor, Adam Wyner, Giuseppe Contissa,
• Abstract summary: We present a modular system for representing and reasoning with legal aspects of traffic rules for autonomous vehicles. We focus on a subset of the United Kingdom's Highway Code (HC) related to junctions.
• Score: 0.1053373860696675
• License:
• Abstract: In this paper, we present a modular system for representing and reasoning with legal aspects of traffic rules for autonomous vehicles. We focus on a subset of the United Kingdom's Highway Code (HC) related to junctions. As human drivers and automated vehicles (AVs) will interact on the roads, especially in urban environments, we claim that an accessible, unitary, high-level computational model should exist and be applicable to both users. Autonomous vehicles introduce a shift in liability that should not bring disadvantages or increased burden on human drivers. We develop a system "in silico" of the model. The proposed system is built of three main components: a natural language interface, using Logical English, which encodes the rules; an internal representation of the rules in Prolog; and an multi-agent-based simulation environment, built in NetLogo. The three components interact: Logical English is translated into and out of Prolog (along with some support code); Prolog and NetLogo interface via predicates. Such a modular approach enables the different components to carry different "burdens" in the overall system; it also allows swapping of modules. Given NetLogo, we can visualize the effect of the modeled rules as well as validate the system with a simple dynamic running scenario. Designated agents monitor the behaviour of the vehicles for compliance and record potential violations where they occur. The information on potential violations is then utilized by Validators, to determine whether the violation is punishable, differentiating between exceptions and cases.

Related papers

• A Framework for Learning Scoring Rules in Autonomous Driving Planning Systems [2.4578723416255754]
  FLoRA is a framework that learns interpretable scoring rules represented in temporal logic. Our approach effectively learns to evaluate driving behavior even though the training data only contains positive examples. Evaluations in closed-loop planning simulations demonstrate that our learned scoring rules outperform existing techniques.
  arXiv  Detail & Related papers  (2025-02-17T02:06:57Z)
• DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving [69.82743399946371]
  DriveMLM is a framework that can perform close-loop autonomous driving in realistic simulators. We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system. This model can plug-and-play in existing AD systems such as Apollo for close-loop driving.
  arXiv  Detail & Related papers  (2023-12-14T18:59:05Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• LLM4Drive: A Survey of Large Language Models for Autonomous Driving [62.10344445241105]
  Large language models (LLMs) have demonstrated abilities including understanding context, logical reasoning, and generating answers. In this paper, we systematically review a research line about textitLarge Language Models for Autonomous Driving (LLM4AD).
  arXiv  Detail & Related papers  (2023-11-02T07:23:33Z)
• Social Occlusion Inference with Vectorized Representation for Autonomous Driving [0.0]
  This paper introduces a novel social occlusion inference approach that learns a mapping from agent trajectories and scene context to an occupancy grid map (OGM) representing the view of ego vehicle. To verify the performance of vectorized representation, we design a baseline based on a fully transformer encoder-decoder architecture. We evaluate our approach on an unsignalized intersection in the INTERACTION dataset, which outperforms the state-of-the-art results.
  arXiv  Detail & Related papers  (2023-03-18T10:44:39Z)
• Advising Autonomous Cars about the Rules of the Road [0.0]
  (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"
  arXiv  Detail & Related papers  (2022-09-28T12:22:59Z)
• An Intelligent Self-driving Truck System For Highway Transportation [81.12838700312308]
  In this paper, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real-world deployment. We also deploy our proposed system on a real truck and conduct real world experiments which shows our system's capacity of mitigating sim-to-real gap.
  arXiv  Detail & Related papers  (2021-12-31T04:54:13Z)
• Algorithmic Ethics: Formalization and Verification of Autonomous Vehicle Obligations [0.0]
  Dominance Act Utilitarianism (DAU) is a suitable starting point for formalizing the obligations of autonomous systems like self-driving cars. We show how obligations can change over time, which is necessary for long-term autonomy.
  arXiv  Detail & Related papers  (2021-05-06T17:41:06Z)
• 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)
• 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.