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News/Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
arxiv
PublishedMay 22, 2026 at 4:00 AM
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Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty

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Publisher summary· verbatim

arXiv:2605.20255v1 Announce Type: cross 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, es

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Mentioned models
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  • 01
    Multi-Agent Proximal Policy Optimization (MAPPO)
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arxiv
Read original ↗All from arxiv →
Tags
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#reinforcement-learning#self-driving-cars#safety-assessment

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