LIDAR-AD：一种用于自动驾驶的无解码器潜在交互梦想家与动作残差链

中文摘要 AI 辅助

自动驾驶需要在动态交通环境中进行长距离闭环决策。潜在世界模型通过在紧凑潜在空间中实现基于想象的决策，为该问题提供了一个有效框架。然而，多源观测包含与控制无关的冗余，而可靠的驾驶决策依赖于与风险相关的关系、未来动态和连续动作调整。这种不匹配使得观测重建和绝对动作建模在学习与决策相关的潜在动态方面次优。我们提出了LIDAR-AD，一种用于自动驾驶的无解码器潜在交互梦想家与动作残差链。LIDAR-AD用减少冗余的潜在对齐取代观测重建，鼓励在多源驾驶输入中紧凑表示与风险相关的关系。它进一步将车辆控制建模为残差动作更新，并使用残差动作序列对比学习将多步残差驱动的展开与未来潜在状态对齐。确定性分析表明，潜在双曲正切残差参数化在保持内部动作可达性的同时，将平滑的长距离控制表示为紧凑的局部更新。这些设计共同改进了风险感知状态抽象、连续控制建模和长距离动态预测。在各种模拟驾驶场景下的广泛实验表明，LIDAR-AD始终优于世界模型基线，在基于学习的方法中实现了最高奖励和最佳成功率。对nuPlan衍生的日志重建场景的评估进一步证明了LIDAR-AD在现实世界交通布局下的可转移性。

英文摘要

Autonomous driving requires long-horizon closedloop decision making in dynamic traffic environments. Latent world models offer an effective framework for this problem by enabling imagination-based decision making in compact latent spaces. However, multi-source observations contain controlirrelevant redundancy, whereas reliable driving decisions rely on risk-relevant relations, future dynamics, and continuous action adjustments. This mismatch makes observation reconstruction and absolute action modeling suboptimal for learning decisionrelevant latent dynamics. We propose LIDAR-AD, a decoderfree Latent-Interaction Dreamer with Action-Residual Chains for autonomous driving. LIDAR-AD replaces observation reconstruction with redundancy-reduced latent alignment, encouraging compact representations of risk-relevant relations in multi-source driving inputs. It further models vehicle control as residual action updates and uses residual-action sequence contrastive learning to align multi-step residual-driven rollouts with future latent states. A deterministic analysis shows that the latent-tanh residual parameterization preserves interior action reachability while representing smooth long-horizon control as compact local updates. Together, these designs improve risk-aware state abstraction, continuous-control modeling, and long-horizon dynamics prediction. Extensive experiments across diverse simulated driving scenarios demonstrate that LIDAR-AD consistently outperforms world-model baselines, achieving the highest reward and the best success rate among learning-based methods. Evaluations on nuPlan-derived log-reconstructed scenarios further demonstrate the transferability of LIDAR-AD under real-world traffic layouts.

发表机构

• School of Mechanical Engineering, Southeast University（东南大学机械工程学院）
• Department of Civil and Environmental Engineering, University of Michigan（密歇根大学土木与环境工程系）
• Xheart Technology Co., Ltd.（芯驰科技有限公司）

机构由 AI 辅助整理，请以论文原文为准。