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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario
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Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario

机译：混合交通场景下基于条件人工势场的自动驾驶汽车安全控制及换道

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摘要

Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehicles’ abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learning-based lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an interval-based support vector machine is designed to predict the vehicles’ lane change intention utilizing limited low-level vehicle status through vehicle-to-vehicle communication. Then, a conditional artificial potential field method is used to design the car-following controller by incorporating the lane-change intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehicle’s lane change intention more accurately. The autonomous vehicle avoids collisions with a lane-changing connected unintelligent vehicle with reliable safety and favorable dynamic performance.
机译：跟车是自动驾驶汽车必不可少的轨迹控制策略，它不仅可以提高交通效率，还可以减少燃油消耗和排放。然而，在相邻车道中的车道改变意图的预测是有问题的，并且将显着影响自动驾驶车辆的跟车控制，尤其是当车辆改变车道仅是连接的非智能车辆而没有昂贵且精确的传感器时。自动驾驶汽车会遭受相邻汽车突然变道的困扰，这可能会降低乘坐舒适性并增加能耗，甚至导致碰撞。针对这一问题，提出了一种基于机器学习的车道变化意图预测和实时自主车辆控制器。首先，基于间隔的支持向量机被设计为通过车对车通信利用有限的低级车辆状态来预测车辆的换道意图。然后，通过结合车辆的变道意图，使用条件人工势场方法来设计跟车控制器。实验结果表明，该方法可以更准确地估算车辆的车道变更意图。该自动驾驶汽车避免了与变道连接的非智能汽车的碰撞，具有可靠的安全性和良好的动态性能。

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