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Model Predictive Control based Stability Control of Autonomous Vehicles on Low Friction Road

机译:基于模型预测控制基于预测控制在低摩擦道路上的自主车辆稳定性控制

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摘要

The challenge lies in developing fully autonomous vehicles is to drive safely in inclement weather. Driving in inclement weather is often a risky task because reacting proactively and stabilizing the vehicle on low friction road is a challenging task unlike driving on high friction road. To tackle such issue, this paper presents a predictive motion framework to operate safely on low friction road without prior knowledge of tire-road friction coefficient. The proposed control algorithm consists of the instability detection algorithm and the longitudinal/lateral motion control algorithms. The instability detection algorithm (1) determines whether the vehicle is stable without knowing friction coefficient and (2) estimates the friction coefficient. The longitudinal and lateral motion control algorithms are separately but interdependently designed based on Model Predictive Control method to proactively control the vehicle with a forecast of vehicle motions. The potential of the proposed approach is shown through computer simulations with a high fidelity vehicle model. The results show that the proposed algorithm (1) successfully reduces speed to avoid path deviation and (2) detects the vehicle instability and stabilize the vehicle due to sudden friction changes.
机译:挑战在于开发完全自治车辆就是安全地在恶劣天气下驾驶。在恶劣天气中驾驶往往是一个冒险的任务,因为在低摩擦道路上积极和稳定车辆的抗议是一个具有挑战性的任务,不同于在高摩擦道路上驾驶。为了解决此类问题,本文提出了一种预测的运动框架,可以在低摩擦道路上安全地操作,而无需先前了解轮胎道路摩擦系数。所提出的控制算法包括不稳定性检测算法和纵向/横向运动控制算法。不稳定性检测算法(1)确定车辆是否稳定而不知道摩擦系数,并且(2)估计摩擦系数。纵向和横向运动控制算法是单独但相互依存的基于模型预测控制方法设计,以主动地控制车辆的预测车辆运动。通过具有高保真性车辆模型的计算机模拟示出了所提出的方法的潜力。结果表明,所提出的算法(1)成功降低了速度以避免路径偏差,并且(2)检测车辆不稳定性并稳定由于突然摩擦变化而稳定车辆。

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