# Residual Chain Prediction for Autonomous Driving Path Planning

arXiv: https://arxiv.org/abs/2404.05423 · HTML: https://mlsanity.com/paper/2404.05423/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 16 of 16)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning](https://mlsanity.com/paper/2207.07601/) (2022)
- [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://mlsanity.com/paper/1904.08980/) (2019)
- [End-to-End Urban Driving by Imitating a Reinforcement Learning Coach](https://mlsanity.com/paper/2108.08265/) (2021)
- [End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances](https://mlsanity.com/paper/1911.10868/) (2019)
- [Learning from All Vehicles](https://mlsanity.com/paper/2203.11934/) (2022)
- [Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients](https://mlsanity.com/paper/2109.13333/) (2021)
- [Reinforcement and Imitation Learning via Interactive No-Regret Learning](https://mlsanity.com/paper/1406.5979/) (2014)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Urban Driving with Conditional Imitation Learning](https://mlsanity.com/paper/1912.00177/) (2019)
- [SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies](https://mlsanity.com/paper/2109.13602/) (2021)
- [GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving](https://mlsanity.com/paper/2111.08575/) (2021)
- [Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms](https://mlsanity.com/paper/2012.00889/) (2020)

## Abstract (arXiv)

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.