Benchmarking Autonomous Driving Planners Across Leaderboards: A Unified CARLA-Based Evaluation

Abstract:Autonomous driving remains a highly active research domain that seeks to enable vehicles to perceive dynamic environments, predict the future trajectories of traffic agents such as vehicles, pedestrians, and cyclists and plan safe and efficient future motions. To advance the field, several competitive platforms and benchmarks have been established to provide standardized datasets and evaluation protocols. Each offers a unique dataset and challenging planning problems spanning a wide range of driving scenarios and conditions. In this study, we present a comparative case study of representative motion planning methods drawn from major benchmark ecosystems, including CARLA, nuPlan, and the Waymo Open Dataset. To ensure a fair and unified evaluation, we adopt CARLA Leaderboard v2.1 as our common evaluation platform and evaluate eight representative methods: TF++, InterFuser, TCP, PDM-Lite, MTR+MPC, CaRL, PlanT 2.0, Diffusion planner. By highlighting the strengths and weaknesses of current approaches, we identify prevailing trends, common challenges, and potential directions for advancing motion-planning research.

Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2509.22754 [cs.RO]

(or arXiv:2509.22754v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2509.22754 Focus to learn more arXiv-issued DOI via DataCite

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