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.
关键词
车道保持 /
模型预测控制 /
小偏差模型 /
驾驶员辅助系统
Key words
lane keeping /
model predictive control /
small deviation model /
driver assistance system
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基金
国家留学基金委赞助项目(201206210103)
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