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Application of Open-Loop Stackelberg Equilibrium to Modeling a Driver's Interaction with Vehicle Active Steering Control in Obstacle Avoidance

机译:开环Stackelberg平衡模型在驾驶员与车辆主动转向控制中的交互作用建模中的应用

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摘要

The increasing use of active front steering (AFS) technology for obstacle avoidance raises the question of drivers' interaction with vehicle automation. Mathematical models capable of representing such interaction are in demand for driver behavior study. This paper presents the application of open-loop Stackelberg equilibrium to modeling a driver's interaction with vehicle AFS control in an obstacle avoidance scenario, where both the driver and the AFS controller are exerting steering control to the vehicle. In this paper, such driver-AFS interactive steering control is modeled as a leader-follower game. Mathematical expressions of the driver's and the AFS controller's steering control strategies are derived using the linear quadratic dynamic optimization approach and the distributed model predictive control (DMPC) approach. These two approaches are found to give identical control gains, which suggest their equivalence in representing driver-AFS interaction. The DMPC approach is found to consume far less computation time due to its numerical nature. Mathematical modifications to the steering control strategies are then introduced to allow practical implementation for a future experimental study. Simulation results including time histories of steering angles and vehicle responses are illustrated and discussed.
机译:主动前转向(AFS)技术越来越多地用于避障,这引发了驾驶员与车辆自动化的交互作用的问题。驾驶员行为研究需要能够表示这种相互作用的数学模型。本文介绍了开环Stackelberg平衡在建模驾驶员在避障情况下与车辆AFS控制的交互时的应用,驾驶员和AFS控制器都在对车辆施加转向控制。在本文中,这种驾驶员-AFS交互式转向控制被建模为领导者跟随者游戏。使用线性二次动态优化方法和分布式模型预测控制(DMPC)方法,得出驾驶员和AFS控制器的转向控制策略的数学表达式。发现这两种方法具有相同的控制增益,这表明它们在表示驱动程序与AFS交互作用方面等效。由于其数字特性,发现DMPC方法消耗的计算时间要少得多。然后引入对转向控制策略的数学修改,以允许在将来的实验研究中进行实际实施。仿真结果包括转向角和车辆响应的时间历史记录。

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