Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back
This addresses safety-critical planning for autonomous vehicles by providing a deployable solution that improves temporal consistency without additional supervision.
The paper tackles the problem of temporal inconsistency in end-to-end autonomous driving planners by introducing Echo Planning, a self-correcting framework that enforces bi-directional consistency between current observations and future trajectories. Experiments on nuScenes show state-of-the-art performance with a 0.04 m reduction in L2 error and 0.12% lower collision rate compared to one-shot planners.
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.