Assessing behaviour coverage in a multi-agent system simulation for autonomous vehicle testing
- URL: http://arxiv.org/abs/2512.23445v1
- Date: Mon, 29 Dec 2025 13:02:32 GMT
- Title: Assessing behaviour coverage in a multi-agent system simulation for autonomous vehicle testing
- Authors: Manuel Franco-Vivo,
- Abstract summary: This study focuses on the behaviour coverage analysis of a multi-agent system simulation designed for autonomous vehicle testing.<n>By defining a set of driving scenarios, and agent interactions, we evaluate the extent to which the simulation encompasses a broad range of behaviours relevant to autonomous driving.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As autonomous vehicle technology advances, ensuring the safety and reliability of these systems becomes paramount. Consequently, comprehensive testing methodologies are essential to evaluate the performance of autonomous vehicles in diverse and complex real-world scenarios. This study focuses on the behaviour coverage analysis of a multi-agent system simulation designed for autonomous vehicle testing, and provides a systematic approach to measure and assess behaviour coverage within the simulation environment. By defining a set of driving scenarios, and agent interactions, we evaluate the extent to which the simulation encompasses a broad range of behaviours relevant to autonomous driving. Our findings highlight the importance of behaviour coverage in validating the effectiveness and robustness of autonomous vehicle systems. Through the analysis of behaviour coverage metrics and coverage-based testing, we identify key areas for improvement and optimization in the simulation framework. Thus, a Model Predictive Control (MPC) pedestrian agent is proposed, where its objective function is formulated to encourage \textit{interesting} tests while promoting a more realistic behaviour than other previously studied pedestrian agents. This research contributes to advancing the field of autonomous vehicle testing by providing insights into the comprehensive evaluation of system behaviour in simulated environments. The results offer valuable implications for enhancing the safety, reliability, and performance of autonomous vehicles through rigorous testing methodologies.
Related papers
- A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems [0.9956658791307307]
We compare four representative testing modalities: Software-in-the-Loop (SiL), Vehicle-in-the-Loop (ViL), Mixed-Reality (MR) and full real-world testing.<n>Our results show that while SiL and ViL setups simplify critical aspects of real-world dynamics and sensing, MR testing improves perceptual realism without compromising safety or control.
arXiv Detail & Related papers (2025-09-26T14:08:53Z) - MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving [85.04826012938642]
MetAdv is a novel adversarial testing platform that enables realistic, dynamic, and interactive evaluation.<n>It supports flexible 3D vehicle modeling and seamless transitions between simulated and physical environments.<n>It enables real-time capture of physiological signals and behavioral feedback from drivers.
arXiv Detail & Related papers (2025-08-04T03:07:54Z) - Test Automation for Interactive Scenarios via Promptable Traffic Simulation [48.240394447516664]
We introduce an automated method to generate realistic and safety-critical human behaviors for AV planner evaluation in interactive scenarios.<n>We parameterize complex human behaviors using low-dimensional goal positions, which are then fed into a promptable traffic simulator, ProSim.<n>To automate test generation, we introduce a prompt generation module that explores the goal domain and efficiently identifies safety-critical behaviors using Bayesian optimization.
arXiv Detail & Related papers (2025-06-01T22:29:32Z) - Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices.
This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
arXiv Detail & Related papers (2024-11-04T09:21:00Z) - Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving [17.27549891731047]
We improve the reliability of agent behaviors by closed-loop fine-tuning of behavior models with reinforcement learning.
Our method demonstrates improved overall performance, as well as improved targeted metrics such as collision rate.
We present a novel policy evaluation benchmark to directly assess the ability of simulated agents to measure the quality of autonomous vehicle planners.
arXiv Detail & Related papers (2024-09-26T23:40:33Z) - Exploring the Causality of End-to-End Autonomous Driving [57.631400236930375]
We propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving.
Our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one.
arXiv Detail & Related papers (2024-07-09T04:56:11Z) - Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems.
LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning.
We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
arXiv Detail & Related papers (2023-11-28T03:13:09Z) - Testing predictive automated driving systems: lessons learned and future
recommendations [0.9005172375036413]
We present and analyze the results of physical tests on proving grounds of several predictive systems in automated driving functions.
Based on our experience in testing predictive automated driving functions, we identify the main limitations of current physical testing approaches.
arXiv Detail & Related papers (2022-04-25T12:10:45Z) - Generating and Characterizing Scenarios for Safety Testing of Autonomous
Vehicles [86.9067793493874]
We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator.
We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project.
We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
arXiv Detail & Related papers (2021-03-12T17:00:23Z) - Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to
the Real World [8.498542964344987]
We present a new approach to automated scenario-based testing of the safety of autonomous vehicles.
Our approach is based on formal methods, combining formal specification of scenarios and safety properties.
arXiv Detail & Related papers (2020-03-17T14:17:52Z)
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.