The invention relates to the technical field of data processing, in particular to an unmanned driving method and system. The method comprises the following steps: firstly, obtaining vehicle position configuration characteristics and half-travel vehicle position configuration characteristics based on distribution characteristics of lane-changing failure vehicle distances of vehicles in second historical data of the lane-changing failure vehicle distances, and obtaining lane-changing failure atypical degrees of the vehicles according to similar conditions of the vehicle position configuration characteristics of different vehicles; and obtaining a lane changing failure descriptor according to the lane changing failure duration and the lane changing failure atypical degree of the vehicle, obtaining sample distances according to the lane changing failure descriptor and the vehicle position configuration characteristics, and clustering the sample distances to obtain different lane changing failure scene categories. And obtaining the timeout scaling factor of the vehicle according to the lane changing failure scene category, training the prediction neural network based on the timeout scaling factor and the half-pass vehicle position configuration features, predicting and adjusting the subsequent automatic lane changing duration of the target user, and improving the driving experience of the user.
本发明涉及数据处理技术领域,具体涉及一种无人驾驶方法及系统。首先基于变道失败车距的第二历史数据中,车辆的变道失败车距的分布特征获得车辆位置组态特征和半程车辆位置组态特征,根据不同车辆的车辆位置组态特征的相似情况获得车辆的变道失败非典型度。根据车辆的变道失败时长和变道失败非典型度获得变道失败描述子,根据变道失败描述子和车辆位置组态特征获得样本距离并聚类,获得不同的变道失败场景类别。根据变道失败场景类别获得车辆的超时时间缩放因子,基于超时时间缩放因子和半程车辆位置组态特征训练预测神经网络,对目标用户后续自动变道时长进行预测与调节,提高用户驾驶体验。
Unmanned driving method and system
一种无人驾驶方法及系统
2023-09-12
Patent
Elektronische Ressource
Chinesisch
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