Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back (EchoP)

Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back
Jintao Sun, Hu Zhang, Gangyi Ding, Zhedong Zheng
Accepted by IEEE Transactions on Multimedia (TMM), 2026 (CCF-A, SCI Q1)

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📢 News

• [2026-03] 🎉 Our paper has been officially accepted by IEEE Transactions on Multimedia (TMM)!
• [Upcoming] ⏳ The official PyTorch implementation and pre-trained models will be released soon. Please stay tuned!

💡 Overview

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.

🚀 To-Do List

We are currently actively cleaning up and organizing the codebase to ensure reproducibility and readability. We plan to release the following components:

• Environment setup instructions
• Data preprocessing scripts for standard autonomous driving datasets (e.g., nuScenes)
• Training code for the EchoP framework
• Evaluation scripts and pre-trained weights for both open-loop and closed-loop (Bench2Drive)
• Visualization tools for the CFC cycle

Code will release soon. Thank you for your patience and interest in our work!

🎓 Citation

If you find our paper or this repository helpful for your research, please consider citing our work (Note: We will update the BibTeX with TMM publication details once available.):

@article{sun2025echo,
  title={Echo planning for autonomous driving: From current observations to future trajectories and back},
  author={Sun, Jintao and Zhang, Hu and Ding, Gangyi and Zheng, Zhedong},
  journal={arXiv preprint arXiv:2505.18945},
  year={2025}
}