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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.22754 |
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| _version_ | 1866915516998746112 |
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| author | Atasever, Merve Liu, Zhuochen Li, Qingpei Shah, Akshay Hitendra Walker, Hans Deshmukh, Jyotirmoy V. Jain, Rahul |
| author_facet | Atasever, Merve Liu, Zhuochen Li, Qingpei Shah, Akshay Hitendra Walker, Hans Deshmukh, Jyotirmoy V. Jain, Rahul |
| contents | 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. Among these, leaderboards by the CARLA organization and nuPlan and the Waymo Open Dataset have become leading benchmarks for assessing motion planning algorithms. Each offers a unique dataset and challenging planning problems spanning a wide range of driving scenarios and conditions. In this study, we present a comprehensive comparative analysis of the motion planning methods featured on these three leaderboards. To ensure a fair and unified evaluation, we adopt CARLA leaderboard v2.0 as our common evaluation platform and modify the selected models for compatibility. By highlighting the strengths and weaknesses of current approaches, we identify prevailing trends, common challenges, and suggest potential directions for advancing motion planning research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22754 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-driving cars: Are we there yet? Atasever, Merve Liu, Zhuochen Li, Qingpei Shah, Akshay Hitendra Walker, Hans Deshmukh, Jyotirmoy V. Jain, Rahul Robotics Artificial Intelligence Computer Vision and Pattern Recognition 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. Among these, leaderboards by the CARLA organization and nuPlan and the Waymo Open Dataset have become leading benchmarks for assessing motion planning algorithms. Each offers a unique dataset and challenging planning problems spanning a wide range of driving scenarios and conditions. In this study, we present a comprehensive comparative analysis of the motion planning methods featured on these three leaderboards. To ensure a fair and unified evaluation, we adopt CARLA leaderboard v2.0 as our common evaluation platform and modify the selected models for compatibility. By highlighting the strengths and weaknesses of current approaches, we identify prevailing trends, common challenges, and suggest potential directions for advancing motion planning research. |
| title | Self-driving cars: Are we there yet? |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.22754 |