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首页> 外文会议>European Control Conference >A Stochastic Model Predictive Control Approach for Driver-Aided Intersection Crossing With Uncertain Driver Time Delay
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A Stochastic Model Predictive Control Approach for Driver-Aided Intersection Crossing With Uncertain Driver Time Delay

机译：具有不确定驾驶时间延迟的驾驶员辅助交叉路口的随机模型预测控制方法

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摘要

We investigate the problem of coordinating human-driven vehicles in road intersections without any traffic lights or signs by issuing speed advices. The vehicles in the intersection are assumed to move along an a priori known path and to be connected via vehicle-to-vehicle communication. The challenge arises with the uncertain driver reaction to a speed advice, especially in terms of the driver reaction time delay, as it might lead to unstable system dynamics. For this control problem, a distributed stochastic model predictive control concept is designed which accounts for driver uncertainties. By optimizing over scenarios, which are sequences of independent and identically distributed samples of the uncertainty over the prediction horizon, we can give probabilistic guarantees on constraint satisfaction. Simulation results demonstrate that the scenario-based approach is able to avoid collisions in spite of uncertainty while the non-stochastic baseline controller is not.
机译：我们调查通过发布速度建议，在没有任何交通灯或迹象的情况下协调道路交叉路口中的人类驱动车辆的问题。假设交叉口中的车辆沿着先验的已知路径移动并且通过车辆到车辆通信连接。不确定的驾驶员对速度建议的反应产生挑战，特别是在驾驶员反应时间延迟方面，因为它可能导致不稳定的系统动态。对于这种控制问题，设计了一种分布式随机模型预测控制概念，其占驾驶员不确定性。通过优化方案，它是在预测地平线上的独立和相同分布的不确定性的分布式样本的序列，我们可以对约束满足提供概率的保障。仿真结果表明，基于方案的方法能够尽管不确定，但在非随机基线控制器不是时，避免碰撞。

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