Research On Intelligent Assisted Driving Control Strategy Based On Traffic Accident Scene

The automobile industry has become a pillar industry in China.In recent years,the number of cars in China has been increasing.Due to the increase in the number of motor vehicles,the congestion of roads and the frequent occurrence of traffic accidents have long been the topic of people’s illnesses,and the incidence of accidents has been reduced.And improving the utilization rate of roads has become the research direction of many scholars and researchers.Many assisted driving strategies have been launched and commercialized,but still have not fundamentally solved the high incidence of traffic accidents.In order to solve this problem,we should proceed from the perspective of traffic accident scenes.From this perspective,this paper uses the surrounding environment information collected by the vehicle to be used as the basis for judgment,combined with the classification of traffic accident scenes,determines the accident scene,and then combines the driver.The physiological data,the integration of these three,optimized the control strategy of the predecessors to get a more optimized control strategy.First of all,this article considers people-car-road as one.People are always the dominant factor of the system.Most of the traffic accidents are caused by the driver’s negligence,and the role of assisted driving is that the driver cannot be timely and correct.When the vehicle is in the state of motion,the vehicle automatically intervenes to keep the vehicle in a safe state.After analyzing the typical traffic accident scene,analyze the traffic accident in the scene and avoid the accident from the characteristics of the accident itself..Because human beings are the core of the whole system,the physiological parameters of the driver,especially the driver’s reaction time,are particularly important when reducing the accident rate.This article uses a large number of real-life experiments to different ages,different driving ages,and different fatigues.The data of the driver’s reaction time is analyzed and summarized.The response time of the driver is quantified by the fuzzy inference method in Matlab,and the quantized reaction time is added to the control algorithm for further algorithm optimization.Secondly,this paper establishes four kinds of mode-controlled self-vehicle motion control algorithms in Simulink.According to the speed,acceleration and actual vehicle spacing of the front and rear vehicles,the state of the vehicle should be judged in real time,so that the corresponding acceleration can be performed in each state.Speed adjustment to ensure safe driving.Finally,the paper establishes the corresponding road environment model in Prescan software,including road trend,road environment,etc.,selects the appropriate vehicle,and combines the control algorithm built in Simulink with the established road environment model to perform hardware-in-the-loop joint simulation.The mode that needs to be entered every moment and adjust accordingly.The rationality and feasibility of this control algorithm and switching logic are analyzed from the final image.