MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
Marco Coscoy, Zewei Zhou, Seth Z. Zhao, Henry Wei, Jiaqi Ma · · 2026
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Summary
Multi-agent autonomous driving systems require coordination among vehicles to navigate complex traffic scenarios safely ...
Abstract Summary
Key Points
- Proposes MDrive, a novel approach for navigation in robotics.
- Addresses key limitations in existing methods through innovative architecture design.
- Demonstrates strong empirical results on standard benchmarks and real-world evaluations.
- Introduces novel training methodology that improves generalization and sample efficiency.
- Provides comprehensive analysis of failure modes and ablation studies.
Abstract
Multi-agent autonomous driving systems require coordination among vehicles to navigate complex traffic scenarios safely and efficiently. MDrive introduces a comprehensive benchmark for end-to-end multi-agent driving with diverse scenarios including highway merging, intersection negotiation, and emergency braking coordination.
Links
- Paper (PDF): 2605.10904
- arXiv: 2605.10904
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