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- 2015
基于小偏差模型预测的车道保持辅助控制
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Abstract:
车道保持辅助系统需要在保证安全性的同时适应驾驶员习惯, 避免不必要的干预。该文通过在线构建人-车系统的小偏差模型, 基于模型预测控制理论, 设计了车道保持辅助控制策略。控制器通过在线求解二次规划问题, 获得矫正转向角, 帮助驾驶员避免无意的车道偏离。根据车辆当前状态, 计算名义预测轨迹。通过将非线性人-车模型围绕名义轨迹逐次线性化, 在线获取人-车系统小偏差模型。通过对系统安全性和驾驶员适应性指标的量化设计, 得到相应的目标函数和I/O约束, 建立了滚动时域优化问题。仿真实验演示了该控制器探测车道偏离危险和转向矫正的过程。真实场景下的实车实验表明: 该系统具有避免车道偏离、适应驾驶员习惯、避免不必要干预的能力。
Abstract：Lane control systems automatically keep a vehicle in its lane to improve driving safety. Such systems need to adapt to the driver's characteristics and should reduce unnecessary intervention. A small deviation model of the human-vehicle system is formulated for on-line prediction of the future vehicle trajectory with an assistance control strategy based on model predictive control (MPC). A corrective steering angle is computed by solving a quadratic programming problem. The nominal trajectory is predicted using the current vehicle information. Then, a deviation model is obtained by successively linearizing the human-vehicle system around the nominal prediction trajectory. A cost function and I/O constraints are designed according to a performance index. Simulations and real world tests show that this approach is able to avoid unintended lane departures while adapting to the driver's driving patterns and avoiding unnecessary intervention.
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