The invention discloses an interaction strategy optimization method for a single intelligent vehicle and a manual driving vehicle, and relates to the field of automatic driving decision and traffic safety. The method comprises the following steps: constructing a car-following model, screening driving data for accurately distinguishing drivers with three different driving styles of careful, moderate and aggressive driving styles through questionnaire survey, dividing automatic driving vehicles in a simulation platform into different driving styles, and constructing a non-signalized intersection mixed traffic flow information incomplete vehicle-to-vehicle game passing decision balancing model. Performing game simulation on the intelligent bicycle main body and different types of automatic driving vehicles conflicting with the intelligent bicycle main body by using a simulation platform to obtain an efficiency-safety optimization scheme; the method can reflect the internal characteristics of a dynamic feedback mechanism and a behavior-risk two-way influence mechanism of the two game parties, and solves the problem that the theoretical modeling depth of the interactive game of the single intelligent automatic driving vehicle and the manual driving vehicle is shallow in the prior art.
本发明公开了一种单车智能车辆与人工驾驶车辆的交互策略优化方法,涉及自动驾驶决策与交通安全领域,包括提取参数;构建跟驰模型,通过问卷调查筛选出准确区别谨慎、适度、激进三种不同驾驶风格的驾驶员的驾驶数据,并将仿真平台中自动驾驶车辆划分为不同驾驶风格,构建无信号交叉口混合交通流信息非完全态的车车博弈通行决策均衡模型,利用仿真平台对单车智能主体车与与其具有冲突的不同类型自动驾驶车辆进行博弈仿真,得出“效率‑安全”优化方案;本方法能够反映博弈双方的动态反馈机制以及行为‑风险双向影响机制的内在特点,解决现有技术当中对单车智能自动驾驶车辆与人工驾驶车辆交互博弈理论建模深度浅的问题。
Interaction strategy optimization method for intelligent single vehicle and manual driving vehicle
一种单车智能车辆与人工驾驶车辆的交互策略优化方法
2024-06-04
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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