  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.
