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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Data-Driven State-Increment Statistical Model and Its Application in Autonomous Driving
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Data-Driven State-Increment Statistical Model and Its Application in Autonomous Driving

机译：数据驱动状态增量统计模型及其在自动驾驶中的应用

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摘要

The aim of trajectory planning is to generate a feasible, collision-free trajectory to guide an autonomous vehicle from the initial state to the goal state safely. However, it is difficult to guarantee that the trajectory is feasible for the vehicle and the real path of the vehicle is collision-free when the vehicle follows the trajectory. In this paper, a state-increment statistical model (SISM) is proposed to describe the kinodynamic constraints of a vehicle by modeling the controller, the actuator, and the vehicle model jointly. The SISM consists of Gaussian distributions of lateral error increments in all state subspaces which are composed of the curvature radius, the velocity, and the lateral error. It is a data-driven modeling approach that can improve the SISM via increasing the number of samples of the increment-state, which is composed of the state and its corresponding increment of the lateral error. According to the SISM, the experience cost functions are designed to evaluate the trajectories for searching the best one with the lowest cost, and the real path can be predicted directly according to the planned trajectory and the vehicle state. The predicted path can be utilized effectually to evaluate the safety of the vehicle motion.
机译：轨迹规划的目的是生成可行的，无碰撞的轨迹，以将自动驾驶车辆从初始状态安全地引导到目标状态。然而，难以保证该轨迹对于车辆是可行的，并且当车辆遵循该轨迹时，车辆的真实路径是无碰撞的。本文提出了一种状态增量统计模型（SISM），通过对控制器，执行器和车辆模型进行联合建模来描述车辆的运动学约束。 SISM由所有状态子空间中横向误差增量的高斯分布组成，该子空间由曲率半径，速度和横向误差组成。它是一种数据驱动的建模方法，可以通过增加增量状态的样本数量来改进SISM，增量状态由状态及其对应的横向误差增量组成。根据SISM，设计了体验成本函数来评估轨迹，以寻找成本最低的最佳轨迹，并且可以根据计划轨迹和车辆状态直接预测真实路径。预测路径可以有效地用于评估车辆运动的安全性。

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