Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912971546951680 |
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| author | Sun, Jintao Zhang, Hu Ding, Gangyi Zheng, Zhedong |
| author_facet | Sun, Jintao Zhang, Hu Ding, Gangyi Zheng, Zhedong |
| contents | Modern end-to-end autonomous driving systems suffer from a critical limitation: their planners lack mechanisms to enforce temporal consistency between predicted trajectories and evolving scene dynamics. This absence of self-supervision allows early prediction errors to compound catastrophically over time. We introduce Echo Planning (EchoP), a new self-correcting framework that establishes an end-to-end Current - Future - Current (CFC) cycle to harmonize trajectory prediction with scene coherence. Our key insight is that plausible future trajectories should be bi-directionally consistent, i.e., not only generated from current observations but also capable of reconstructing them. The CFC mechanism first predicts future trajectories from the Bird's-Eye-View (BEV) scene representation, then inversely maps these trajectories back to estimate the current BEV state. By enforcing consistency between the original and reconstructed BEV representations through a cycle loss, the framework intrinsically penalizes physically implausible or misaligned trajectories. Experiments on nuScenes show that the proposed method yields competitive performance, reducing L2 error (Avg) by -0.04 m and collision rate by -0.12% compared to one-shot planners. Moreover, EchoP seamlessly extends to closed-loop evaluation, i.e., Bench2Drive, attaining a 26.54% success rate. Notably, EchoP requires no additional supervision: the CFC cycle acts as an inductive bias that stabilizes long-horizon planning. Overall, EchoP offers a simple, deployable pathway to improve reliability in safety-critical autonomous driving. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back Sun, Jintao Zhang, Hu Ding, Gangyi Zheng, Zhedong Computer Vision and Pattern Recognition Robotics Modern end-to-end autonomous driving systems suffer from a critical limitation: their planners lack mechanisms to enforce temporal consistency between predicted trajectories and evolving scene dynamics. This absence of self-supervision allows early prediction errors to compound catastrophically over time. We introduce Echo Planning (EchoP), a new self-correcting framework that establishes an end-to-end Current - Future - Current (CFC) cycle to harmonize trajectory prediction with scene coherence. Our key insight is that plausible future trajectories should be bi-directionally consistent, i.e., not only generated from current observations but also capable of reconstructing them. The CFC mechanism first predicts future trajectories from the Bird's-Eye-View (BEV) scene representation, then inversely maps these trajectories back to estimate the current BEV state. By enforcing consistency between the original and reconstructed BEV representations through a cycle loss, the framework intrinsically penalizes physically implausible or misaligned trajectories. Experiments on nuScenes show that the proposed method yields competitive performance, reducing L2 error (Avg) by -0.04 m and collision rate by -0.12% compared to one-shot planners. Moreover, EchoP seamlessly extends to closed-loop evaluation, i.e., Bench2Drive, attaining a 26.54% success rate. Notably, EchoP requires no additional supervision: the CFC cycle acts as an inductive bias that stabilizes long-horizon planning. Overall, EchoP offers a simple, deployable pathway to improve reliability in safety-critical autonomous driving. |
| title | Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2505.18945 |