DistillDrive：基于同构异源规划模型的端到端多模式自动驾驶蒸馏
DistillDrive: End-to-End Multi-Mode Autonomous Driving Distillation by Isomorphic Hetero-Source Planning Model
AI总结：
提出DistillDrive，通过知识蒸馏和强化学习，利用教师模型的多样化规划实例增强端到端自动驾驶的多模式学习，在nuScenes和NAVSIM上碰撞率降低50%，闭环性能提升3点。
摘要：
端到端自动驾驶近年来发展迅速，对工业界和学术界都产生了深远影响。然而，现有工作过度关注自车状态作为其唯一学习目标，且缺乏面向规划的理解，这限制了整体决策过程的鲁棒性。在本工作中，我们提出了DistillDrive，一种基于知识蒸馏的端到端自动驾驶模型，利用多样化实例模仿来增强多模式运动特征学习。具体而言，我们采用基于结构化场景表示的规划模型作为教师模型，将其多样化的规划实例作为端到端模型的多目标学习目标。此外，我们引入强化学习以增强状态到决策映射的优化，同时利用生成建模构建面向规划的实例，促进潜在空间内的复杂交互。我们在nuScenes和NAVSIM数据集上验证了我们的模型，与基线模型相比，碰撞率降低了50%，闭环性能提升了3个百分点。代码和模型已在https://github.com/YuruiAI/DistillDrive公开提供。
英文摘要：
End-to-end autonomous driving has been recently seen rapid development, exerting a profound influence on both industry and academia. However, the existing work places excessive focus on ego-vehicle status as their sole learning objectives and lacks of planning-oriented understanding, which limits the robustness of the overall decision-making prcocess. In this work, we introduce DistillDrive, an end-to-end knowledge distillation-based autonomous driving model that leverages diversified instance imitation to enhance multi-mode motion feature learning. Specifically, we employ a planning model based on structured scene representations as the teacher model, leveraging its diversified planning instances as multi-objective learning targets for the end-to-end model. Moreover, we incorporate reinforcement learning to enhance the optimization of state-to-decision mappings, while utilizing generative modeling to construct planning-oriented instances, fostering intricate interactions within the latent space. We validate our model on the nuScenes and NAVSIM datasets, achieving a 50\% reduction in collision rate and a 3-point improvement in closed-loop performance compared to the baseline model. Code and model are publicly available at https://github.com/YuruiAI/DistillDrive
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
