The invention relates to the technical field of traffic conflict control, and discloses a traffic conflict identification and risk level prediction method and system based on deep learning, and the method comprises the steps: obtaining the three-dimensional point cloud information of different target vehicles based on laser radar data; performing trajectory tracking on the three-dimensional point cloud information of different target vehicles to obtain different target vehicle trajectories; according to different target vehicle tracks, target vehicle traffic features are extracted; constructing a training set and a test set according to the traffic characteristics of the target vehicle and a preset conflict detection threshold value; training the classification model based on the training set to obtain a trained classification model; in the training process, a Bayesian search algorithm is adopted to adjust and optimize hyper-parameters; and inputting the traffic characteristics of the to-be-identified vehicle and the adjacent vehicle into the trained classification model to obtain an identification result of whether the to-be-identified vehicle has a traffic conflict and a traffic conflict level. Scientific decision support is provided for a traffic management department, and the traffic safety level is effectively improved.
本发明涉及交通冲突管控技术领域,公开了基于深度学习的交通冲突识别与风险等级预测方法及系统,方法包括:基于激光雷达数据得到不同目标车辆的三维点云信息;对不同目标车辆的三维点云信息进行轨迹跟踪,得到不同目标车辆轨迹;根据不同目标车辆轨迹,提取出目标车辆交通特征;根据目标车辆交通特征和预设的冲突检测阈值,构建训练集和测试集;基于训练集对分类模型进行训练,得到训练后的分类模型;训练的过程中采用贝叶斯搜索算法对超参数进行调优;将待识别车辆及其相邻车辆的交通特征,输入到训练后的分类模型中,得到待识别车辆是否发生交通冲突的识别结果和交通冲突等级。本发明为交通管理部门提供科学决策支持,有效提高交通安全水平。
Traffic conflict identification and risk level prediction method and system based on deep learning
基于深度学习的交通冲突识别与风险等级预测方法及系统
2024-10-01
Patent
Electronic Resource
Chinese
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