让多车协作直接优化驾驶决策，提升自动驾驶表现。
Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving
- 端到端系统直接优化规划任务，融合时空信息与多车特征。
- 在闭环评估中驾驶得分79.72，比基准高3.33%。
- 适合关注车联网自动驾驶协同决策的研究者。
车联万物辅助的自动驾驶（V2X-AD）通过信息共享显著提升驾驶性能。然而，现有协同感知方法仅优化模块级感知能力，未能有效服务于最终的规划与控制任务。本文提出一种端到端协同驾驶系统，直接优化规划任务表现。系统采用MotionNetwork融合历史时序信息，利用注意力机制将空间特征高效压缩为紧凑标记，并通过自回归解码器自适应融合多车特征。此外，引入混合专家（MoE）架构增强模型对异构特征的表征能力。实验表明，该方法在闭环评估中取得79.72的驾驶得分，优于当前最优基线CoDriving（77.15），提升3.33%，同时保持通信效率。
原文摘要 · Abstract (English)
Vehicle-to-everything-aided autonomous driving (V2X-AD) significantly enhances driving performance through information sharing. However, existing collaborative perception methods only optimize module-level perception capabilities and fail to effectively serve the ultimate planning and control tasks. We propose an end-to-end collaborative driving system that directly optimizes planning task performance. The system employs MotionNetwork to fuse historical temporal information, utilizes attention mechanisms to efficiently compress spatial features into compact tokens, and adaptively fuses multi-agent features through an autoregressive decoder. Additionally, we introduce Mixture-of-Experts (MoE) architecture to enhance the model's representation capacity for heterogeneous features. Experiments demonstrate that our method achieves a driving score of 79.72, surpassing the state-of-the-art CoDriving baseline (77.15) by 3.33% in closed-loop evaluation while maintaining communication efficiency.
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