论文标题
实时运动计划使用受限的迭代线性二次调节器进行自动驾驶
Real Time Motion Planning Using Constrained Iterative Linear Quadratic Regulator for On-Road Self-Driving
论文作者
论文摘要
避免碰撞是人们为开发自动驾驶技术所需考虑的最具挑战性的任务之一。在本文中，我们提出了一种新的时空运动计划算法，该算法有效地使用迭代线性二次调节器（ILQR）有效地解决了受约束的非线性最佳控制问题（ILQR），并考虑到不确定的驾驶行为，该行为的交通工具的不确定驾驶行为可以使自动驾驶汽车之间的交通型车辆（以启用车辆为准），并且可以将EGO的车辆与“ Ego”（Ego）访问，并且“ Ego”（Ego）的车辆是“ EGO”的“ EGO”（EGO）。与所有周围车辆的距离足够大，可以实现所需的碰撞避免交通动作。为此，我们介绍了“碰撞多边形”的概念，用于计算自我车辆和交通车辆之间的最小距离，并通过正确对交通工具的行为进行正确建模以评估碰撞风险，提供两种不同的解决方案来设计运动计划问题的约束。最后，在多个实时任务中，使用模拟器和3级自动驾驶测试平台避免了碰撞，可以在多个实时任务中验证ILQR运动计划算法。
Collision avoidance is one of the most challenging tasks people need to consider for developing the self-driving technology. In this paper we propose a new spatiotemporal motion planning algorithm that efficiently solves a constrained nonlinear optimal control problem using the iterative linear quadratic regulator (iLQR), which takes into account the uncertain driving behaviors of the traffic vehicles and minimizes the collision risks between the self-driving vehicle (referred to as the "ego" vehicle) and the traffic vehicles such that the ego vehicle is able to maintain sufficiently large distances to all the surrounding vehicles for achieving the desired collision avoidance maneuver in traffic. To this end, we introduce the concept of the "collision polygon" for computing the minimum distances between the ego vehicle and the traffic vehicles, and provide two different solutions for designing the constraints of the motion planning problem by properly modeling the behaviors of the traffic vehicles in order to evaluate the collision risk. Finally, the iLQR motion planning algorithm is validated in multiple real-time tasks for collision avoidance using both a simulator and a level-3 autonomous driving test platform.
