AI 中文总结
研究车路协同辅助自动驾驶中现有方法不足，提出端到端协同驾驶系统，用MotionNetwork融合信息、注意力机制压缩特征、自回归解码器融合多智能体特征及引入MoE架构，提升驾驶分数并保持通信效率。
AI 中文摘要
车路协同辅助自动驾驶（V2X-AD）通过信息共享显著提高驾驶性能。然而，现有的协作感知方法仅优化模块级感知能力，无法有效服务于最终的规划和控制任务。我们提出了一种直接优化规划任务性能的端到端协同驾驶系统。该系统采用MotionNetwork融合历史时间信息，利用注意力机制将空间特征高效压缩为紧凑令牌，并通过自回归解码器自适应融合多智能体特征。此外，我们引入专家混合（MoE）架构来增强模型对异构特征的表示能力。实验表明，我们的方法在闭环评估中实现了79.72的驾驶分数，比现有最优的CoDriving基线（77.15）高出3.33%，同时保持了通信效率。
英文摘要
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.
CommentsAccepted at IEEE ICME 2026
