The invention provides a secondary traffic accident prevention and control method in an intelligent network connection hybrid traffic flow environment. And after the accident occurs, the traffic management center evaluates the accident influence and the upstream traffic state based on the real-time traffic flow data acquired by the camera. On the one hand, RSU equipment within the range of 2000 meters on the upstream of the accident section is controlled to release safety information, and downstream accident conditions are informed to a network connection vehicle in advance through vehicle-vehicle communication forwarding. On the other hand, variable signal identifiers at different sections and RSU equipment are controlled to issue variable speed limit control instructions together, upstream coming vehicles are guided to decelerate ahead of an upstream free flow section so as to reduce the risk of secondary accidents, and the traffic capacity of urban expressways is improved. The control instruction is not forwarded through vehicle-to-vehicle communication. The control model is optimized based on a deep reinforcement learning algorithm. Two indexes of safety and efficiency are considered when a reward function is set, and a penalty factor is set to guide the model to learn a simpler and more reasonable control strategy. The model is optimized by adopting an actor reviewer framework.
本发明提出了智能网联混合交通流环境下二次交通事故预防控制方法。在事故发生后,交管中心基于摄像头获取的实时交通流数据评估事故影响与上游交通状态。一方面控制事故断面上游2000米范围内的RSU设备发布安全信息,并通过车车通信转发,向网联车提前告知下游事故状况。另一方面控制不同断面处的可变信号标识与RSU设备共同发布可变限速控制指令,引导上游来车提前于上游自由流段提前减速以降低二次事故风险,并提高城市快速路通行能力。控制指令不通过车车通信转发。本发明基于深度强化学习算法优化控制模型。设置奖励函数时兼顾安全与效率两项指标,并设置惩罚因子以引导模型学习更简洁合理的控制策略。模型采用演员‑评论家框架优化。
Secondary traffic accident prevention and control method in intelligent network connection mixed traffic flow environment
一种智能网联混合交通流环境下二次交通事故预防控制方法
2021-01-15
Patent
Electronic Resource
Chinese
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