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：在密集交通场景下，确保安全性同时保持自动驾驶任务的高性能是一项关键挑战。为解决这一问题，本文提出一种计算高效的时空滚动时域控制(ST-RHC)方案，在控制空间中生成安全、动态可行且节能的轨迹，从而在实时条件下高精度、高安全性地完成密集交通中的不同驾驶任务。特别地，本文设计了一种考虑主动交互的具身化时空安全屏障模块，以缓解其他车辆轨迹预测不准确性带来的影响。随后，运动规划与控制问题被构建为带约束的非线性优化问题，该公式化方法有利于结合多重打靶法高效利用现成的优化求解器。通过在合成与真实交通数据集上针对密集交通场景与现有最优算法的全面对比，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。