VADv2：通过概率规划实现端到端向量自主驾驶
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
AI总结：
本文提出VADv2，一种基于概率规划的端到端自主驾驶模型，通过概率场函数建模动作空间到概率分布的映射，利用大规模驾驶演示提升闭合回路性能，优于现有方法。
摘要：
从大规模驾驶演示中学习人类驾驶策略具有前景，但规划的不确定性和非确定性使这一任务具有挑战性。现有基于学习的规划方法遵循确定性范式直接回归动作，无法应对不确定性问题。本文提出一种概率规划模型，称为VADv2，用于端到端自主驾驶。我们采用概率场函数来建模从动作空间到概率分布的映射。由于规划动作空间是一个高维连续时空空间，难以处理，我们首先将规划动作空间离散化为一个大的规划词汇库，然后将规划词汇库分词为规划令牌。规划令牌与场景令牌交互并输出动作的概率分布。大规模驾驶演示用于监督分布。VADv2在CARLA Town05基准上实现了最先进的闭环性能，显著优于现有方法，并在最近的Bench2Drive基准中领先。我们进一步在NAVSIM和一个大规模3DGS基准上进行综合评估，证明其在现实应用中的有效性。代码可在https://github.com/hustvl/VAD上获得。
英文摘要：
Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging. Existing learning-based planning methods follow a deterministic paradigm to directly regress the action, failing to cope with the uncertainty problem. In this work, we propose a probabilistic planning model for end-to-end autonomous driving, termed VADv2. We resort to a probabilistic field function to model the mapping from the action space to the probabilistic distribution. Since the planning action space is a high-dimensional continuous spatiotemporal space and hard to tackle, we first discretize the planning action space to a large planning vocabulary and then tokenize the planning vocabulary into planning tokens. Planning tokens interact with scene tokens and output the probabilistic distribution of action. Mass driving demonstrations are leveraged to supervise the distribution. VADv2 achieves state-of-the-art closed-loop performance on the CARLA Town05 benchmark, significantly outperforming existing methods, and also leads the recent Bench2Drive benchmark. We further provide comprehensive evaluations on NAVSIM and a large-scale 3DGS-based benchmark, demonstrating its effectiveness in real-world applications. Code is available at https://github.com/hustvl/VAD.
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。
