商用半挂汽车纵向避撞控制OA北大核心CSTPCD
Research on longitudinal collision avoidance control of commercial semi-trucks
为降低商用半挂汽车在雨天湿滑路面等危险条件下的追尾事故发生率,基于魔术公式轮胎模型及递推最小二乘算法(RLS)估计路面附着系数,利用BP神经网络预测制动距离,提出一种基于实时制动距离预测(BDP)的安全距离模型,设计了分级避撞控制策略.分别在高低附着系数路面的前车制动(CCRb)、前车静止(CCRs)、前车低速行驶(CCRm)3 种工况下进行联合仿真,验证模型有效性.仿真结果表明:高附着系数路面上BDP模型和Mazda模型在3 种工况下能够成功避免碰撞,制动结束时BDP模型的停车间距更小,更符合驾驶习惯且兼顾一定的行车效率及驾乘舒适性;低附着系数路面上Mazda模型仅在CCRm工况时成功避撞,BDP模型在3 种工况下可以顺利避免碰撞的发生.BDP模型可以有效降低低附着系数路面的追尾事故发生率.
To reduce the occurrences of rear-end accidents of commercial semi-trucks in hazardous situations like slippery roads on rainy days,this paper proposes a safe distance model based on real-time braking distance prediction(BDP)by employing the magic formula tire model and recursive least squares algorithm(RLS)to estimate the road adhesion coefficient and BP neural network for the prediction of the braking distance.The hierarchical collision avoidance control strategy is designed.The co-simulation of CCRb,CCRs and CCRm on high and low adhesion coefficient pavement is conducted to verify the effectiveness of the model.Our simulation results show the BDP and Mazda models effectively avoid collisions under three working conditions on the road surfaces with high adhesion coefficients.The BDP model achieves a shorter stopping distance at the end of braking,better in line with the driving habits and meeting the requirements for driving efficiency and comfort.On the road surfaces with low adhesion coefficients,the Mazda model only effectively avoids collisions in CCRm working conditions whereas the BDP model successfully shuns collisions in three working conditions.The BDP model effectively reduces the occurrences of rear-end accidents on pavements with low adhesion coefficients.
陈佳浩;魏民祥;李军;王成东;徐志欣;邢致毓
南京航空航天大学 能源与动力学院,南京 210016南京航空航天大学 能源与动力学院,南京 210016||中国人民解放军32228部队22分队,南京 210012
交通运输
半挂汽车路面附着系数估计BP神经网络制动距离预测纵向避撞控制
semi-truckspavement adhesion coefficient estimationBP neural networkbraking distance predictionlongitudinal collision avoidance control
《重庆理工大学学报》 2024 (013)
50-58 / 9
国家自然科学基金项目(52072116)
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