End-to-End Driving with Online Trajectory Evaluation via BEV World Model
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908309418672128 |
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| author | Li, Yingyan Wang, Yuqi Liu, Yang He, Jiawei Fan, Lue Zhang, Zhaoxiang |
| author_facet | Li, Yingyan Wang, Yuqi Liu, Yang He, Jiawei Fan, Lue Zhang, Zhaoxiang |
| contents | End-to-end autonomous driving has achieved remarkable progress by integrating perception, prediction, and planning into a fully differentiable framework. Yet, to fully realize its potential, an effective online trajectory evaluation is indispensable to ensure safety. By forecasting the future outcomes of a given trajectory, trajectory evaluation becomes much more effective. This goal can be achieved by employing a world model to capture environmental dynamics and predict future states. Therefore, we propose an end-to-end driving framework WoTE, which leverages a BEV World model to predict future BEV states for Trajectory Evaluation. The proposed BEV world model is latency-efficient compared to image-level world models and can be seamlessly supervised using off-the-shelf BEV-space traffic simulators. We validate our framework on both the NAVSIM benchmark and the closed-loop Bench2Drive benchmark based on the CARLA simulator, achieving state-of-the-art performance. Code is released at https://github.com/liyingyanUCAS/WoTE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | End-to-End Driving with Online Trajectory Evaluation via BEV World Model Li, Yingyan Wang, Yuqi Liu, Yang He, Jiawei Fan, Lue Zhang, Zhaoxiang Computer Vision and Pattern Recognition End-to-end autonomous driving has achieved remarkable progress by integrating perception, prediction, and planning into a fully differentiable framework. Yet, to fully realize its potential, an effective online trajectory evaluation is indispensable to ensure safety. By forecasting the future outcomes of a given trajectory, trajectory evaluation becomes much more effective. This goal can be achieved by employing a world model to capture environmental dynamics and predict future states. Therefore, we propose an end-to-end driving framework WoTE, which leverages a BEV World model to predict future BEV states for Trajectory Evaluation. The proposed BEV world model is latency-efficient compared to image-level world models and can be seamlessly supervised using off-the-shelf BEV-space traffic simulators. We validate our framework on both the NAVSIM benchmark and the closed-loop Bench2Drive benchmark based on the CARLA simulator, achieving state-of-the-art performance. Code is released at https://github.com/liyingyanUCAS/WoTE. |
| title | End-to-End Driving with Online Trajectory Evaluation via BEV World Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.01941 |