Autonomous Driving Benchmarks

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13 papers in the last four weeks, up 86% on the four weeks before. 0.1% of all new papers.

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Latest papers 74

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MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
May 11, 2026Marco Coscoy, Zewei Zhou, Seth Z. Zhao +9Autonomous Driving Benchmarks Autonomous Driving
University of California, Los Angeles
Is Your Driving World Model an All-Around Player?
May 11, 2026Lingdong Kong, Ao Liang, Tianyi Yan +20Autonomous Driving Benchmarks Physical Consistency in Video Generation
NUS · UM · USTC +10
Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving
May 11, 2026Aron Distelzweig, Faris Janjoš, Andreas Look +7Autonomous Driving Benchmarks Self-Play RL
University of Freiburg · Bosch Center for Artificial Intelligence · Coburg University of Applied Sciences +1
HiDrive: A Closed-Loop Benchmark for High-Level Autonomous Driving
May 11, 2026Zhongyu Xia, Guanyu Zhu, Guo Tang +2Autonomous Driving Benchmarks End-to-End Autonomous Driving
Wangxuan Institute of Computer Technology, Peking University
DRIVE-C: A Controlled Corruption Dataset for Autonomous Driving
May 10, 2026Shiva AherAutonomous Driving Datasets Autonomous Driving Benchmarks
Georgia Institute of Technology, Georgia, United States
CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography
May 6, 2026Gasser Elazab, Frank Neuhaus, Tilman Koß +5Autonomous Driving Datasets Autonomous Driving Benchmarks
1CARIAD SE · 2Technische Universität Berlin · 3Vision & Robotics GmbH
Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drive
Apr 30, 2026Yiru Wang, Anqing Jiang, Shuo Wang +4Autonomous Driving Benchmarks Autonomous Driving Safety Evaluation
Bosch Corporate Research, Bosch (China) Investment Ltd., Shanghai, China
TRACE: Topology-aware Reconstruction of Accidents in CARLA for AV Evaluation
Apr 23, 2026Nahian Salsabil, Sebastian ElbaumAutonomous Driving Datasets Autonomous Driving Benchmarks
University of Virginia Charlottesville, Virginia, USA
OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge
Apr 22, 2026Yuhang Zhang, Jiarui Zhang, Bowen Jian +6Autonomous Driving Datasets Autonomous Driving Benchmarks
College of Transportation, Tongji University, Shanghai 201804, China.
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
Apr 22, 2026Finn Rasmus Schäfer, Yuan Gao, Dingrui Wang +5VLM Evaluation Autonomous Driving Benchmarks
Professorship of Autonomous Vehicle Systems, Technical University of Munich, Munich, Germany · Bayerische Motoren Werke AG, Munich, Germany · Data Analytics and Machine Learning Group, Technical University of Munich, Munich, Germany
Driving Like Yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving
Feb 21, 2026Xiaoru Dong, Ruiqin Li, Xiao Han +7Autonomous Driving Benchmarks End-to-End Autonomous Driving
The University of Hong Kong · ShanghaiTech University · The Chinese University of Hong Kong
Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads
Feb 18, 2026Daniil Lisus, Katya M. Papais, Cedric Le Gentil +4Autonomous Driving Datasets Autonomous Driving Benchmarks
University of Toronto Institute of Aerospace Studies, Toronto, Canada · Trimble, Canada
LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving
May 1, 2025Zhijie Qiao, Haowei Li, Zhong Cao +1VLM Evaluation Autonomous Driving Benchmarks
Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA · University of Michigan Transportation Research Institute, Ann Arbor, MI 48109, USA
ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving
Nov 3, 2024Salman Khan, Izzeddin Teeti, Reza Javanmard Alitappeh +5Autonomous Driving Datasets Autonomous Driving Benchmarks
Oxford Brookes University · MAZUST · University of Oxford +2

May 11, 2026, cs.ROJ/K move · Enter open · S save

MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems

Marco Coscoy, Zewei Zhou, Seth Z. Zhao, Henry Wei+8

University of California, Los Angeles

Autonomous Driving Benchmarks Autonomous Driving V2X Communication Cooperative Perception Autonomous Driving Simulation

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Vehicle-to-Everything (V2X) communication has emerged as a promising paradigm for autonomous driving, enabling connected agents to share complementary perception information and negotiate with each other to benefit the final planning. Existing V2X benchmarks, however, fall short in two ways: (i) open-loop evaluations fail to capture the inherently closed-loop nature of driving, leading to evaluation gaps, and (ii) current closed-loop evaluations lack behavioral and interactive diversity to reflect real-world driving. Thus, it is still unclear the extent of benefits of multi-agent systems for closed-loop driving. In this paper, we introduce MDrive, a closed-loop cooperative driving benchmark comprising 225 scenarios grounded in both NHTSA pre-crash typologies and real-world V2X datasets. Our benchmark results demonstrate that multi-agent systems are generally better than single-agent counterparts. However, current multi-agent systems still face two important challenges: (i) perception sharing enhances perceptions, but doesn't always translate to better planning; (ii) negotiation improves planning performance but harms it in complex and dense traffic scenarios. MDrive further provides an open-source toolbox for scenario generation, Real2Sim conversion, and human-in-the-loop simulation. Together, MDrive establishes a reproducible foundation for evaluating and improving the generalization and robustness of cooperative driving systems.

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