Planning safe and efficient trajectories through signal-free intersections presents significant challenges for autonomous vehicles (AVs), particularly in dynamic, multi-task environments with unpredictable interactions and an increased possibility of conflicts. This study aims to address these challenges by developing a unified, robust, adaptive framework to ensure safety and efficiency across three distinct intersection movements: left-turn, right-turn, and straight-ahead. Existing methods often struggle to reliably ensure safety and effectively learn multi-task behaviors from demonstrations in such environments. This study proposes a safety-critical planning method that integrates Dynamic High-Order Control Barrier Functions (DHOCBF) with a diffusion-based model, called Dynamic Safety-Critical Diffuser (DSC-Diffuser). The DSC-Diffuser leverages task-guided planning to enhance efficiency, allowing the simultaneous learning of multiple driving tasks from real-world expert demonstrations. Moreover, the incorporation of goal-oriented constraints significantly reduces displacement errors, ensuring precise trajectory execution. To further ensure driving safety in dynamic environments, the proposed DHOCBF framework dynamically adjusts to account for the movements of surrounding vehicles, offering enhanced adaptability and reduce the conservatism compared to traditional control barrier functions. Validity evaluations of DHOCBF, conducted through numerical simulations, demonstrate its robustness in adapting to variations in obstacle velocities, sizes, uncertainties, and locations, effectively maintaining driving safety across a wide range of complex and uncertain scenarios. Comprehensive performance evaluations demonstrate that DSC-Diffuser generates realistic, stable, and generalizable policies, providing flexibility and reliable safety assurance in complex multi-task driving scenarios.

翻译：在无信号交叉口规划安全高效的轨迹对自动驾驶车辆提出了重大挑战，尤其是在动态、多任务环境中，存在不可预测的交互作用和冲突可能性增加的问题。本研究旨在通过开发一个统一、鲁棒、自适应的框架来解决这些挑战，以确保左转、右转和直行这三种不同交叉口运动的安全与效率。现有方法往往难以可靠地确保安全，并难以在此类环境中从演示中有效学习多任务行为。本研究提出了一种安全关键规划方法，将动态高阶控制屏障函数与基于扩散的模型相结合，称为动态安全关键扩散器。DSC-Diffuser利用任务引导规划来提高效率，允许从现实世界的专家演示中同时学习多个驾驶任务。此外，面向目标的约束的引入显著减少了位移误差，确保了精确的轨迹执行。为了进一步确保动态环境中的驾驶安全，所提出的DHOCBF框架能够动态调整以适应周围车辆的运动，与传统控制屏障函数相比，提供了更强的适应性并减少了保守性。通过数值模拟对DHOCBF进行的有效性评估表明，其在适应障碍物速度、尺寸、不确定性和位置变化方面具有鲁棒性，能够在各种复杂和不确定场景中有效保持驾驶安全。全面的性能评估表明，DSC-Diffuser能够生成逼真、稳定且可泛化的策略，在复杂的多任务驾驶场景中提供灵活性和可靠的安全保证。