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.

在自动驾驶系统领域，路径规划算法的改进对于车辆在动态环境中尤其是复杂城市场景中的导航至关重要。本研究引入了新概念的 Residual Chain Loss，通过动态调整损失计算过程来增强预测路径点的时间依赖性和准确性，显著提高模型性能，并且与端到端路径规划框架无缝集成，有效解决了传统曼哈顿距离在行为克隆中导致的协变量漂移问题，强调了 Residual Chain Loss 在自动驾驶系统中规划成分的潜力，是实现第 5 级自动驾驶系统的重大进展。