In dense traffic scenarios, ensuring safety while keeping high task performance for autonomous driving is a critical challenge. To address this problem, this paper proposes a computationally-efficient spatiotemporal receding horizon control (ST-RHC) scheme to generate a safe, dynamically feasible, energy-efficient trajectory in control space, where different driving tasks in dense traffic can be achieved with high accuracy and safety in real time. In particular, an embodied spatiotemporal safety barrier module considering proactive interactions is devised to mitigate the effects of inaccuracies resulting from the trajectory prediction of other vehicles. Subsequently, the motion planning and control problem is formulated as a constrained nonlinear optimization problem, which favorably facilitates the effective use of off-the-shelf optimization solvers in conjunction with multiple shooting. The effectiveness of the proposed ST-RHC scheme is demonstrated through comprehensive comparisons with state-of-the-art algorithms on synthetic and real-world traffic datasets under dense traffic, and the attendant outcome of superior performance in terms of accuracy, efficiency and safety is achieved.

翻译：在密集交通场景中，确保安全性同时保持自主驾驶的高任务性能是一项关键挑战。针对该问题，本文提出一种计算高效的时空滚动时域控制（ST-RHC）方案，在控制空间内生成安全、动态可行且节能的轨迹，以高精度和高安全性实时完成密集交通中的不同驾驶任务。特别地，设计了一种考虑主动交互的具身时空安全屏障模块，以减轻由其他车辆轨迹预测不准确性带来的影响。随后，将运动规划与控制问题建模为约束非线性优化问题，这有利于有效利用现成的优化求解器与多重打靶法相结合。通过在与密集交通场景下的合成数据集和真实交通数据集上，与最先进算法进行全面比较，验证了所提ST-RHC方案的有效性，并在精度、效率与安全性方面取得了优越性能。