Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
A new study explores the use of multi-agent reinforcement learning (MARL) to improve the safety of autonomous vehicles in unpredictable pedestrian environments. The research indicates that co-training self-driving cars with pedestrians can significantly reduce collision rates compared to traditional methods. The findings suggest that incorporating pedestrian behavioral uncertainty leads to more realistic and effective safety assessments.
- ▪The study introduces a MARL environment where a self-driving car and 12 pedestrians are co-trained.
- ▪In evaluations, the co-trained self-driving car achieved a 78% goal completion rate with a 14% collision rate.
- ▪Jaywalking accounted for 13% of crossing events but was linked to 62% of collisions.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20255 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Machine Learning arXiv:2605.20255 (cs) [Submitted on 18 May 2026] Title:Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Authors:Prakash Aryan, Kaushik Raghupathruni, Timo Kehrer, Sebastiano Panichella View a PDF of the paper titled Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty, by Prakash Aryan and 3 other authors View PDF HTML (experimental) 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.
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