This paper presents our 2nd place solution for the NuPlan Challenge 2023. Autonomous driving in real-world scenarios is highly complex and uncertain. Achieving safe planning in the complex multimodal scenarios is a highly challenging task. Our approach, Imitation with Spatial-Temporal Heatmap, adopts the learning form of behavior cloning, innovatively predicts the future multimodal states with a heatmap representation, and uses trajectory refinement techniques to ensure final safety. The experiment shows that our method effectively balances the vehicle's progress and safety, generating safe and comfortable trajectories. In the NuPlan competition, we achieved the second highest overall score, while obtained the best scores in the ego progress and comfort metrics.
@article{arxiv.2306.15700,
title = {Imitation with Spatial-Temporal Heatmap: 2nd Place Solution for NuPlan Challenge},
author = {Yihan Hu and Kun Li and Pingyuan Liang and Jingyu Qian and Zhening Yang and Haichao Zhang and Wenxin Shao and Zhuangzhuang Ding and Wei Xu and Qiang Liu},
journal= {arXiv preprint arXiv:2306.15700},
year = {2023}
}