HiDrive: A Closed-Loop Benchmark for High-Level Autonomous Driving
2026-05-11 • Robotics
RoboticsComputer Vision and Pattern Recognition
AI summaryⓘ
The authors explain that current self-driving car tests are too easy and don’t cover rare but important situations, like unusual objects or tricky emergency decisions. They created HiDrive, a new testing system that includes these rare cases and checks if cars follow rules, make moral choices, and handle emergencies properly. HiDrive also uses better graphics and physics to make the tests more realistic. This helps see if self-driving cars are really ready for the complex real world.
end-to-end autonomous drivingbenchmarklong-tail scenariosdecision-makingtraffic-rule compliancemoral reasoningemergency responseclosed-loop evaluationphysics enginevisual rendering
Authors
Zhongyu Xia, Guanyu Zhu, Guo Tang, Wenhao Chen, Yongtao Wang
Abstract
End-to-end autonomous driving has witnessed rapid progress, yet existing benchmarks are increasingly saturated, with state-of-the-art models achieving near-perfect scores on widely used open-loop and closed-loop benchmarks. This saturation does not mean that the problem has been solved; instead, it reveals that current benchmarks remain limited in scenario diversity, object variety, and the breadth of driving capabilities they evaluate. In particular, they lack sufficient long-tail scenarios involving rare but safety-critical objects and fail to assess advanced decision-making such as legal compliance, ethical reasoning, and emergency response. To address these gaps, we propose HiDrive, a new closed-loop benchmark for end-to-end autonomous driving that emphasizes long-tail scenarios and a richer evaluation of driving capabilities. HiDrive introduces a diverse set of rare objects and uncommon traffic situations, and expands evaluation from basic driving skills to more advanced capabilities, including rule compliance, moral reasoning, and context-dependent emergency maneuvers. Correspondingly, we extend previous collision-avoidance-centered metrics into a comprehensive evaluation system that encompasses collision and braking, traffic-rule compliance, and moral-reasoning indicators. Built on a more advanced physics engine, HiDrive provides physically realistic lighting and high-fidelity visual rendering, offering a more challenging and realistic testbed for assessing whether autonomous driving systems can handle the complexity of real-world deployment. The HiDrive software, source code, digital assets, and documentation are available at https://github.com/VDIGPKU/HiDrive.