CauTraj：一种融合因果知识的车道变换轨迹规划框架
CauTraj: A Causal-Knowledge-Guided Framework for Lane-Changing Trajectory Planning of Autonomous Vehicles
AI总结：
CauTraj通过整合因果知识提升自动驾驶车辆车道变换轨迹规划的精度与稳定性。
摘要：
增强车道变换车辆轨迹规划器的性能是自动驾驶在人机混合交通中的关键挑战之一。大多数现有研究在设计轨迹规划模型时没有纳入人类司机的先验知识。为了解决这个问题，本研究提出了一种新的轨迹规划框架，将因果先验知识整合到控制过程中。车辆的纵向和横向微观行为被建模以量化交互风险，并构建了一个分阶段的因果图以捕捉车道变换场景中的因果依赖性。然后使用因果推断估计车道变换车辆与周围车辆之间的因果效应，包括平均因果效应（ATE）和条件平均处理效应（CATE）。这些因果先验被嵌入到模型预测控制（MPC）框架中以增强轨迹规划。所提出的方法在自然驾驶车辆轨迹数据集上进行了验证。实验结果表明：（1）因果推断提供了可解释且稳定的车辆交互量化；（2）个体因果效应揭示了驾驶员异质性；（3）与基线MPC相比，所提出的方法更接近人类驾驶行为，最大轨迹偏移量从1.2米减少到0.2米，横向速度波动减少60%，转向角波动减少50%。这些发现为类人轨迹规划提供了方法论支持，并为提高自动驾驶车辆测试和交通模拟平台的安全性、稳定性和现实性提供了实际价值。
英文摘要：
Enhancing the performance of trajectory planners for lane - changing vehicles is one of the key challenges in autonomous driving within human - machine mixed traffic. Most existing studies have not incorporated human drivers' prior knowledge when designing trajectory planning models. To address this issue, this study proposes a novel trajectory planning framework that integrates causal prior knowledge into the control process. Both longitudinal and lateral microscopic behaviors of vehicles are modeled to quantify interaction risk, and a staged causal graph is constructed to capture causal dependencies in lane-changing scenarios. Causal effects between the lane-changing vehicle and surrounding vehicles are then estimated using causal inference, including average causal effects (ATE) and conditional average treatment effects (CATE). These causal priors are embedded into a model predictive control (MPC) framework to enhance trajectory planning. The proposed approach is validated on naturalistic vehicle trajectory datasets. Experimental results show that: (1) causal inference provides interpretable and stable quantification of vehicle interactions; (2) individual causal effects reveal driver heterogeneity; and (3) compared with the baseline MPC, the proposed method achieves a closer alignment with human driving behaviors, reducing maximum trajectory deviation from 1.2 m to 0.2 m, lateral velocity fluctuation by 60%, and yaw angle variability by 50%. These findings provide methodological support for human-like trajectory planning and practical value for improving safety, stability, and realism in autonomous vehicle testing and traffic simulation platforms.
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。
