Imitation with Spatial-Temporal Heatmap: 2nd Place Solution for NuPlan Challenge
This work addresses safe and comfortable trajectory generation for autonomous vehicles in real-world driving, though it is incremental as it builds on behavior cloning with novel heatmap and refinement techniques.
The paper tackled autonomous driving planning in complex multimodal scenarios by predicting future states with a heatmap representation and refining trajectories for safety, achieving second place in the NuPlan Challenge 2023 with top scores in ego progress and comfort metrics.
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.