In this article, we propose an optimization-based integrated behavior planning and motion control scheme, which is an interpretable and adaptable urban autonomous driving solution that complies with complex traffic rules while ensuring driving safety. Inherently, to ensure compliance with traffic rules, an innovative design of potential functions (PFs) is presented to characterize various traffic rules related to traffic lights, traversable and non-traversable traffic line markings, etc. These PFs are further incorporated as part of the model predictive control (MPC) formulation. In this sense, high-level behavior planning is attained implicitly along with motion control as an integrated architecture, facilitating flexible maneuvers with safety guarantees. Due to the well-designed objective function of the MPC scheme, our integrated behavior planning and motion control scheme is competent for various urban driving scenarios and able to generate versatile behaviors, such as overtaking with adaptive cruise control, turning in the intersection, and merging in and out of the roundabout. As demonstrated from a series of simulations with challenging scenarios in CARLA, it is noteworthy that the proposed framework admits real-time performance and high generalizability.

翻译：本文提出了一种基于优化的集成行为规划与运动控制方案，作为一种可解释且可适应的城市自动驾驶解决方案，能够在确保驾驶安全的同时遵循复杂交通规则。本质上，为确保交通规则合规性，我们创新性地设计了势函数（PFs）来表征与交通信号灯、可通行及不可通行交通标线等相关的各类交通规则。这些势函数进一步被整合到模型预测控制（MPC）框架中。在此意义上，高层行为规划与运动控制以隐式方式集成实现，有助于在安全保障下进行灵活机动。由于MPC方案中目标函数的精心设计，我们的集成行为规划与运动控制方案能够胜任各类城市驾驶场景，并生成多样化行为，例如带自适应巡航控制的超车、路口转弯、以及环岛进出等。通过在CARLA中一系列具有挑战性场景的仿真验证结果表明，该框架具备实时性能与高度泛化能力。