论文/arXiv 2610.18167·15 天前

超越规划：控制在环的可微端到端自动驾驶

DIVA将可微识别车辆动力学嵌入端到端驾驶训练，打通感知到车辆状态链路，并在闭环Bench2Drive上验证。

AI 整理

• 硬件零部件
• 本体 SDK 与控制
• 端侧部署

原文链接：Beyond Planning: Differentiable Control-in-the-loop End-to-End Autonomous Driving（arXiv 2610.18167 · 发布日期：2026-10-13）

Beyond Planning: Differentiable Control-in-the-loop End-to-End Autonomous Driving

DIVA将可微识别车辆动力学嵌入端到端驾驶训练，打通感知到车辆状态链路，并在闭环Bench2Drive上验证。

核心要点

• 端到端驾驶可直接规划轨迹，但手调跟踪控制器会阻断车辆运动到网络的梯度。
• DIVA把识别的执行器级车辆模型嵌入训练，形成从感知到车辆状态的可微链。
• 学习式控制解码器以滚动状态监督，并用速率一致参数化适配控制与重规划频率。
• 在闭环Bench2Drive上驾驶得分86.7，较既有方法高3.3，平均jerk近乎减半。

验证范围与局限：验证范围为闭环Bench2Drive基准及部署时显著车辆模型失配下的路径跟踪。

英文摘要 (Abstract)

End-to-end autonomous driving has achieved remarkable progress by planning trajectories directly from raw sensor data. Yet the step from planning to control still relies on hand-tuned tracking controllers that block the gradient path from vehicle motion back to the network. This gap stems from non-differentiable vehicle dynamics, aliased supervision from coarsely sampled expert labels, rate mismatch between high-rate control and low-rate re-planning, and vehicle model discrepancies between training and deployment. This paper proposes DIVA (Differentiable Identified Vehicle dynamics for end-to-end Autonomous driving) to close this gap. An identified actuator-level vehicle model is embedded in the training loop of the end-to-end driving network, forming a differentiable chain from perception to vehicle states. A learning-based control decoder replaces the external controller and is supervised on the rolled-out vehicle states, bypassing the aliased control-input labels. A rate-consistent control-input parameterization reconciles the heterogeneous sampling rates and enables asynchronous execution with a 3.8 ms control-only cycle. On-policy re-identification and an online disturbance observer address training-time distribution shift and deployment-time model mismatch respectively. On the closed-loop Bench2Drive benchmark, DIVA achieves a driving score of 86.7, surpassing the widely adopted state-of-the-art approach by 3.3 points while nearly halving mean jerk and reducing brake switching by 64%, and maintains superior path tracking accuracy under significant vehicle model mismatch at deployment.

作者：Zhidong Wang, Zirui Li, Zitong Shan, Zhiqi Mao, Qihang Huang, Zhan Chen, Liang JingSong, Han Yu, Chen Lv

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.18167：Beyond Planning: Differentiable Control-in-the-loop End-to-End Autonomous Driving访问 2026-10-21