论文标题
自动驾驶汽车的混蛋限制速度计划：线性编程方法
Jerk Constrained Velocity Planning for an Autonomous Vehicle: Linear Programming Approach
论文作者
论文摘要
在复杂的环境中为自动驾驶车辆的速度规划是最具挑战性的任务之一。它必须满足以下三个要求：关于碰撞的安全；尊重由流量规则定义的最大速度限制；乘客的舒适。为了实现这些目标，应考虑混蛋和动态对象，但是，它使该问题与非凸优化问题一样复杂。在本文中，我们提出了一个基于线性编程（LP）的速度计划方法，该方法具有猛烈的限制和自主驾驶系统的障碍避免限制。为了确认所提出的方法的效率，与几种基于优化的方法进行了比较，我们表明我们的方法可以生成速度曲线，该速度曲线比比较方法更有效地满足上述要求。此外，我们测试了测试场上实际车辆上的算法，以验证所提出的方法的有效性。
Velocity Planning for self-driving vehicles in a complex environment is one of the most challenging tasks. It must satisfy the following three requirements: safety with regards to collisions; respect of the maximum velocity limits defined by the traffic rules; comfort of the passengers. In order to achieve these goals, the jerk and dynamic objects should be considered, however, it makes the problem as complex as a non-convex optimization problem. In this paper, we propose a linear programming (LP) based velocity planning method with jerk limit and obstacle avoidance constraints for an autonomous driving system. To confirm the efficiency of the proposed method, a comparison is made with several optimization-based approaches, and we show that our method can generate a velocity profile which satisfies the aforementioned requirements more efficiently than the compared methods. In addition, we tested our algorithm on a real vehicle at a test field to validate the effectiveness of the proposed method.
