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.

翻译：在快速发展的自动驾驶系统领域，路径规划算法的优化对于在动态环境（尤其是复杂城市场景）中导航车辆至关重要。传统路径规划算法严重依赖静态规则和手动定义参数，在此类情境下往往表现不足，凸显了对更具适应性的学习型方法的需求。其中，行为克隆因其简洁高效而在端到端路径规划中成为一种值得关注的策略。然而，当采用传统曼哈顿距离作为度量标准时，行为克隆面临协变量偏移等挑战。针对这一问题，本研究引入了新颖的“残余链损失”概念。残余链损失通过动态调整损失计算过程，增强预测路径点的时间依赖性和准确性，在不增加额外计算开销的情况下显著提升模型性能。通过在nuScenes数据集上的测试，我们证实了该方法在应对协变量偏移、促进动态损失调整以及确保与端到端路径规划框架无缝集成方面的显著进步。我们的研究结果凸显了残余链损失在革新自动驾驶系统规划组件方面的潜力，标志着在迈向L5级自动驾驶系统的征途中迈出了重要一步。