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- 2018
基于时间延迟动态预测的自动驾驶控制
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Abstract:
由于驾驶过程中的延迟和前视距离等因素，无人车无法准确跟踪规划轨迹。该文通过选择简化的自行车车辆模型，在纯跟踪模型的基础上对原有的算法进行优化，提出了一种基于动态延迟预测的自动驾驶控制方法。通过车辆运动学模型预测延迟后的车辆运动方向和位置信息，并根据行驶方向和轨迹方向之间的偏差值，获得最佳前视距离。MATLAB仿真结果表明，改进的算法可以以7 m/s的行驶速度跟踪规划轨迹，平均误差可以控制在0.3 m以内，跟踪性能优于传统的纯跟踪方法。
Abstract：Signal delays, limited frontal view distances and other factors during self-driving limit the ability of self-driving cars to accurately track their planning trajectory. A simplified bicycle model was used to optimize a classical pure tracking model in an automatic driving control method based on dynamic delay prediction. A vehicle kinematics model is used to predict the vehicle motion direction and position after the delay. The optimal front sight distance is obtained according to difference between driving the actual direction and the tracking direction. MATLAB simulations show that this algorithm can track the planning trajectory at a maximum speed of 7 m/s with the average error controlled to within 0.3 m. Thus, the tracking performance is better than the traditional pure pursuit method.
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