Residual Chain Prediction for Autonomous Driving Path Planning
Liguo Zhou, Yirui Zhou, Huaming Liu, Alois Knoll
TL;DR
This work tackles covariate shift in end-to-end autonomous driving path planning by introducing Residual Chain Loss, a dynamic, temporal loss mechanism that uses relative path increments $ ext{Δ}x_t$ and $ ext{Δ}y_t$ and adjusts ground-truth targets based on prior predictions. By reformulating outputs as relative increments and enabling test-time accumulation via $x_t' = x_0 + \sum_{i=1}^t \text{Δ}x_i'$ and $y_t' = y_0 + \sum_{i=1}^t \text{Δ}y_i'$, the method preserves computation while enhancing temporal fidelity. The approach is validated on the nuScenes dataset, showing substantial improvements over traditional relative-coordinate methods and demonstrating robust compatibility with end-to-end path planning. Overall, Residual Chain Loss advances autonomous driving planning by reducing covariate shift, improving trajectory prediction accuracy, and facilitating integration into learning-based planning pipelines.
Abstract
In the rapidly evolving field of autonomous driving systems, the refinement of path planning algorithms is paramount for navigating vehicles through dynamic environments, particularly in complex urban scenarios. Traditional path planning algorithms, which are heavily reliant on static rules and manually defined parameters, often fall short in such contexts, highlighting the need for more adaptive, learning-based approaches. Among these, behavior cloning emerges as a noteworthy strategy for its simplicity and efficiency, especially within the realm of end-to-end path planning. However, behavior cloning faces challenges, such as covariate shift when employing traditional Manhattan distance as the metric. Addressing this, our study introduces the novel concept of Residual Chain Loss. Residual Chain Loss dynamically adjusts the loss calculation process to enhance the temporal dependency and accuracy of predicted path points, significantly improving the model's performance without additional computational overhead. Through testing on the nuScenes dataset, we underscore the method's substantial advancements in addressing covariate shift, facilitating dynamic loss adjustments, and ensuring seamless integration with end-to-end path planning frameworks. Our findings highlight the potential of Residual Chain Loss to revolutionize planning component of autonomous driving systems, marking a significant step forward in the quest for level 5 autonomous driving system.