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

TL;DR

End-to-end autonomous driving planners struggle with temporal consistency, causing trajectory errors to compound as scene dynamics evolve. Echo Planning introduces a bidirectional Current → Future → Current (CFC) BEV cycle that predicts a future trajectory and then inverts it to reconstruct the current BEV, enforcing cycle-consistency without additional supervision. The approach combines a sparse BEV scene representation with a self-supervised loss that couples forward trajectory prediction to backward BEV reconstruction, achieving state-of-the-art results on nuScenes with reductions in both L2 error (≈0.04 m) and collision rate (≈0.12%) compared to one-shot baselines. This yields a deployable, safety-oriented enhancement for autonomous driving stacks by embedding temporal coherence directly into planning, with potential extensions to longer horizons and multi-modal inputs in future work.

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, a novel self-correcting framework that establishes a closed-loop Current - Future - Current (CFC) cycle to harmonize trajectory prediction with scene coherence. Our key insight is that plausible future trajectories must be bi-directionally consistent, ie, 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 demonstrate state-of-the-art performance, reducing L2 error by 0.04 m and collision rate by 0.12% compared to one-shot planners. Crucially, our method requires no additional supervision, leveraging the CFC cycle as an inductive bias for robust planning. This work offers a deployable solution for safety-critical autonomous systems.

Figures (4)

• Figure 1: Comparison between one-shot paradigms and our echo planning paradigms. (a), (b), and (c) represent different types of one-shot approaches. Both (a) and (b) construct scene representations from image features and rely on auxiliary tasks to supervise the model, thereby enhancing environmental understanding; their main distinction lies in whether dense BEV features are used. Method (c) discards the auxiliary tasks introduced in (b) and is among the first to highlight the importance of temporal supervision, though it still considers only forward verification and thus remains within the one-shot paradigm. In contrast, ours (d) presents the echo planning approach, which employs a Current $\rightarrow$ Future $\rightarrow$ Current cycle. This design enforces bidirectional self-supervision, allowing the model to validate scene understanding without additional auxiliary tasks.
• Figure 2: The overview of our Echo Planning framework. Echo Planning is trained through two complementary loops. The forward loop, indicated by black arrows in the figure, predicts future BEV features through the sparse scene representation model linavigation and applies self-supervision against the ground-truth BEV of future frames. The echo loop, shown in red, takes those predicted future features via the Motion-aware Layer Normalization (MLN) module wang2023exploringlinavigation and TokenFuser ryoo2021tokenlearnerlinavigation, reconstructs the current BEV, and self-supervises against the ground-truth BEV of the current frame. At no point are extra tasks or annotations introduced. By validating perception in both directions along the Current → Future → Current (CFC) cycle, the model cross-checks its understanding of the surrounding scene and thus produces reliable trajectory plans.
• Figure 3: Visualization of planning trajectories in different scenes. We visualize several driving scenes, overlaying the trajectories predicted by our method alongside those of one of the one-shot planners, i.e., GenAD zheng2024genad, and the ground-truth trajectories. Map context is rendered directly from the dataset annotations. In each figure, the green icon denotes the ego vehicle, while red icons mark surrounding vehicles and other dynamic objects.
• Figure 4: Visualization of failure cases. We highlight two distinct planning failure cases. The first arises when the navigation cue in the ground-truth annotation is ambiguous, and the second occurs in open spaces where the predicted trajectory deviates during a turn. In each figure, the green icon denotes the ego vehicle, while red icons mark surrounding vehicles and other dynamic objects.