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PREDICTION METHOD IN AUTOMATIC DRIVING FOR PERFORMING LANE CHANGE TO OVERTAKE LEADING VEHICLE IN TARGET LANE

机译:用于执行车道改变的自动驾驶预测方法,以超越目标车道的领导车辆

摘要

Disclosed is a prediction method in automatic driving for performing lane change to overtake a leading vehicle in a target lane. Said method comprises: acquiring driving state data, in a current sampling period, of a vehicle preparing to perform lane change, a vehicle I, a vehicle II and a vehicle III, the vehicle I being the vehicle just in front of the vehicle preparing to perform lane change in the current lane of the vehicle preparing to perform lane change, and the vehicle II and the vehicle III being respectively the first nearest vehicle and the second nearest vehicle in front of the vehicle preparing to perform lane change in a target lane; calculating, by using the driving state data of the vehicles, the willingness to perform lane change of the vehicle preparing to perform lane change to overtake the vehicle II; and when the willingness exceeds a willingness threshold, predicting that the vehicle preparing to perform lane change will perform lane change to overtake the vehicle II at a future time. In the present invention, the prediction method in automatic driving for performing lane change to overtake a leading vehicle in a target lane effectively excludes factors of surrounding vehicles interfering with a vehicle's decision making, improves the decision certainty of the vehicle, effectively reduces the data analysis and calculation amount of a prediction module in an automatic driving system, reduces the operation difficulty and operation costs of the automatic driving system, and effectively improves the lag and delay of decision making.
机译:公开的是用于进行车道变换的目标车道超车前车在自动驾驶的预测方法。所述方法包括:获取行驶状态的数据,在当前采样周期中,车辆的准备执行车道变更,车辆I,车辆II和车辆III,I是车辆刚刚在车辆准备的前车执行在车辆准备执行车道变更的当前车道改变车道,并且车辆II和车辆III分别是第一最接近车辆和车辆准备执行在目标车道变更车道的前方的第二最近的车辆;计算,通过使用车辆的驱动状态的数据,愿意执行车辆准备执行车道变更至超车车辆II的车道变更;而当超过意愿意愿的阈值,预测车辆准备进行变道将进行变道在未来的时间超车车辆II。在本发明中,用于进行车道改变在自动驾驶的预测方法在一个目标车道超车先行车辆周围的车辆与车辆的决策干扰的有效排除因素,提高了车辆的决定确定性,有效地减少了数据分析并且在自动驾驶系统中的预测模块的计算量,降低了操作的难度和自动驾驶系统的操作成本,并有效地提高了滞后和决策的延迟。

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