Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back
Jintao Sun, Hu Zhang, Gangyi Ding, Zhedong Zheng
TL;DR
Echo Planning introduces a self-correcting cycle that enforces bi-directional consistency between current observations and future trajectory predictions, enhancing stability and safety in autonomous driving systems.
Contribution
The paper presents EchoP, a novel self-supervised framework that uses a CFC cycle to improve trajectory prediction consistency without extra supervision.
Findings
Reduces average L2 error by 0.04 meters.
Decreases collision rate by 0.12%.
Achieves 26.54% success in closed-loop evaluation.
Abstract
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…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
- Introduces a cycle-consistency self-supervision scheme to enhances temporal coherence with bidirectional consistency in the planning process. - Demonstrates improvements across open- and closed-loop evaluations, supported by ablations on loss weighting and token size. - The formulation is conceptually intuitive and integrates easily into existing sparse token planning frameworks such as SSR.
- The paper omits discussion of recent, related works on temporal consistency in end-to-end planning, notably BridgeAD (CVPR 2025) and MomAD (CVPR 2025). While these methods differ in formulation (historical aggregation, momentum modeling) they share similar motivations. - The conceptual advance over SSR is modest. It is unclear whether explicit cycle consistency is essential, as no alternative reconstruction formulations were tested. For instance, directly predicting the previous BEV (t−1) or a
- Sufficient technical details. The CFC cycle is a creative and intuitive way to enforce bidirectional temporal consistency without extra labels, distinguishing it from prior one-shot planners. - Strong empirical results. EchoP achieves SOTA performance on both open-loop (nuScenes) and closed-loop (Bench2Drive) benchmarks, with notable collision rate reductions. - Exhaustive Ablation studies. Comprehensive ablations on loss weights, token numbers, and CFC cycle inclusion demonstrate methodolog
- Limited novelty. The CFC cycle is conceptually interesting, but the core modules (BEVFormer, TokenLearner, TokenFuser, MLN) are borrowed from prior works. It seem that the main contribution of EchoP is the additional branch for reversed reconstruction compared to SSR, which is somewhat limited. - Underexplored theoretical grounding: The bidirectional consistency idea is intuitive but lacks formal justification or comparison with alternative consistency losses (e.g., contrastive, adversarial).
1. Overall presentation: The paper is well-organized and clearly presented, with informative figures, well-structured tables, and clear writing. The overall presentation quality makes the paper easy to follow and understand. 2. Experiments and visualization: The experimental section is comprehensive. The visualizations are clear and intuitive, effectively demonstrating the qualitative performance.
1. **Limited novelty**: This paper is highly similar to SSR [1] and LAW [2], with only minor modifications to the loss calculation for the predicted current BEV feature. The overall contribution appears largely story-driven and incremental, which may not meet the novelty threshold for a top-tier conference such as ICLR. 2. **Unconvincing experiments**: The experiments on the nuScenes dataset have been widely questioned by previous works [3], and the results on the Bench2Drive benchmark are infe
1. The proposed strategy EchoP is clearly described and well visualized, making the algorithm easy to understand. 2. The paper conducts quantitative experiments on the nuScenes and Bench2Drive datasets, showing noticeable improvements on Bench2Drive.
1. In the abstract, Figure 1(d), and Table 1, the paper seems to give the impression that the proposed method is self-supervised and does not rely on auxiliary task supervision (such as detection or mapping) shown in Figure 1(a,b). However, as I understand it, the BEV module still depends on these supervised tasks. This is also mentioned in line 161: “…are transformed into a BEV representation using BEVFormer…”, where BEVFormer indeed relies on such supervision to generate BEV features. Therefor
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Taxonomy
TopicsRobotic Path Planning Algorithms · Robotics and Sensor-Based Localization · Advanced Vision and Imaging