As vehicle automation advances, motion planning algorithms face escalating challenges in achieving safe and efficient navigation. Existing Advanced Driver Assistance Systems (ADAS) primarily focus on basic tasks, leaving unexpected scenarios for human intervention, which can be error-prone. Motion planning approaches for higher levels of automation in the state-of-the-art are primarily oriented toward the use of risk- or anti-collision constraints, using over-approximates of the shapes and sizes of other road users to prevent collisions. These methods however suffer from conservative behavior and the risk of infeasibility in high-risk initial conditions. In contrast, our work introduces a novel multi-objective trajectory generation approach. We propose an innovative method for constructing risk fields that accommodates diverse entity shapes and sizes, which allows us to also account for the presence of potentially occluded objects. This methodology is integrated into an occlusion-aware trajectory generator, enabling dynamic and safe maneuvering through intricate environments while anticipating (potentially hidden) road users and traveling along the infrastructure toward a specific goal. Through theoretical underpinnings and simulations, we validate the effectiveness of our approach. This paper bridges crucial gaps in motion planning for automated vehicles, offering a pathway toward safer and more adaptable autonomous navigation in complex urban contexts.

翻译：随着车辆自动化技术的进步，运动规划算法在实现安全高效导航方面面临日益严峻的挑战。现有高级驾驶辅助系统（ADAS）主要聚焦于基本任务，将意外场景留给人类干预，而人类干预往往容易出错。当前自动化程度较高的运动规划方法，主要倾向于使用风险约束或防碰撞约束，通过对其他道路使用者的形状和尺寸进行过近似处理来防止碰撞。然而，这些方法存在保守行为问题，且在初始风险较高时可能面临不可行性风险。相比之下，我们的工作提出了一种新颖的多目标轨迹生成方法。我们创新性地提出了一种构建风险场的方法，该方法能够适应不同实体形状和尺寸，从而也可考虑潜在被遮挡对象的存在。该方法论被集成到遮挡感知轨迹生成器中，能够在复杂环境中实现动态且安全的机动操作，同时预测（可能隐藏的）道路使用者，并沿基础设施向特定目标行驶。通过理论支撑和仿真验证，我们证实了该方法的有效性。本文填补了自动驾驶车辆运动规划领域的关键空白，为在复杂城市环境中实现更安全、更自适应的自主导航提供了路径。