Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
arXiv:2605.20255v1 Announce Type: new Abstract: Simulation-based testing of self-driving cars (SDCs) typically relies on scripted or simplified pedestrian models that do not capture the heterogeneity and uncertainty of real human crossing behavior. This limits the realism of safety assessments, especially in scenarios involving jaywalking, which is governed by latent personality traits that the vehicle cannot observe. We hypothesize that jointly training pedestrians and the SDC with multi-agent reinforcement learning (MARL) produces more realistic interaction scenarios than training the SDC ag
The increasing sophistication of multi-agent reinforcement learning (MARL) techniques allows for more realistic and complex simulation environments, moving beyond simplistic models of human behavior.
This research directly addresses a critical safety and ethical challenge for autonomous vehicles, enabling more robust testing and development against unpredictable real-world human interactions.
The methodology for training and validating autonomous vehicle behavior, particularly in complex urban scenarios with human unpredictability, becomes significantly more advanced and realistic.
- · Autonomous vehicle developers
- · AI simulation companies
- · Consumers of self-driving cars
- · AI safety researchers
- · Companies relying on simplistic simulation models
- · Traditional rule-based autonomous driving systems
Autonomous vehicles will become safer and more capable of navigating complex, unpredictable human environments.
This improved safety could accelerate public acceptance and regulatory approval of higher levels of autonomous driving.
Greater adoption of autonomous vehicles could reduce traffic accidents attributed to human error and transform urban planning and transportation infrastructure.
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Read at arXiv cs.LG