论文标题
自动驾驶汽车的自动置置不确定性下的安全速度控制和碰撞概率估计
Safe Speed Control and Collision Probability Estimation Under Ego-Pose Uncertainty for Autonomous Vehicle
论文作者
论文摘要
为了使自动驾驶汽车成为智能运输生态系统的一部分，它们必须保证特定的安全水平。为此，需要开发安全的车辆控制算法，其中包括评估沿给定轨迹驾驶的碰撞的可能性，并选择最小化此概率的控制信号。在本文中，我们提出了一个速度控制系统，该系统估算了静态和动态障碍以及自我置孔不确定性的碰撞概率，并选择了最大的安全速度。为此，控制系统将计划的轨迹转换为形成动态车辆模型输入的控制信号。该模型预测了真正的车辆路径。为每个粒子生成预测的轨迹 - 这是由围绕车辆姿势的定位系统的概率假设加权。根据预测的粒子的轨迹，计算碰撞的可能性，并决定最大安全速度。拟议的算法已在实际的自动驾驶汽车上进行了验证。实验结果表明，提出的速度控制系统在执行操作并通过狭窄的开口行驶时将车辆速度降低到安全价值。因此，该系统的行为是在困难和模棱两可的交通情况下驾驶时模仿人类驾驶员的行为。
In order for autonomous vehicles to become a part of the Intelligent Transportation Ecosystem, they are required to guarantee a particular level of safety. For that to happen a safe vehicle control algorithms need to be developed, which include assessing the probability of a collision while driving along a given trajectory and selecting control signals that minimize this probability. In this paper, we propose a speed control system that estimates a collision probability taking into account static and dynamic obstacles as well as ego-pose uncertainty and chooses the maximum safe speed. For that, the planned trajectory is converted by the control system into control signals that form input for the dynamic vehicle model. The model predicts a real vehicle path. The predicted trajectory is generated for each particle -- a weighted by a probability hypothesis of the localization system about the vehicle pose. Based on the predicted particles' trajectories, the probability of collision is calculated, and a decision is made on the maximum safe speed. The proposed algorithm was validated on the real autonomous vehicle. The experimental results demonstrate that the proposed speed control system reduces the vehicle speed to a safe value when performing maneuvers and driving through narrow openings. Therefore the observed behavior of the system is mimicking a human driver behavior when driving in difficult and ambiguous traffic situations.
