| With the development of national economy and the improvement of people’s living standards,automobile has become an indispensable means of transportation in people’s daily life.While automobile facilitates people’s travel,the traffic accidents caused by automobile cause great harm to people’s property and life.The research of automobile active safety control has become a hot spot.The development of artificial intelligence and computer automation control technology promotes the application of intelligent control in the automotive field.As one of the advanced safety technologies,active collision avoidance control system can improve the active safety of vehicles and reduce the incidence of traffic accidents.In this paper,the research on active collision avoidance control system of intelligent vehicle based on deep learning has important theoretical significance and application prospects.In this paper,the intelligent vehicle active collision avoidance system is taken as the research object,the vehicle system dynamics model is analyzed and studied,the driving safety estimation and collision avoidance strategy are studied based on the deep learning algorithm neural network algorithm,the safety distance simulation model is built and the active collision avoidance control system is designed,and then the active collision avoidance system studied in this paper is analyzed by simulation experiment and model experiment The system is tested and verified.The main contents of this paper are as follows:First of all,according to the theory of vehicle safety control and the active collision avoidance control methods at home and abroad,this paper studies the overall scheme of the vehicle active collision avoidance control system based on the analysis of the overall structure and dynamic model of the vehicle,and focuses on the analysis of the key technologies in the scheme.Secondly,the deep learning algorithm of artificial intelligence convolution neural network algorithm is used to study the control algorithm of intelligent vehicle collision avoidance control system.The deep learning algorithm is applied to the perception of the surrounding environment in the process of vehicle driving,and the driving safety estimation and collision avoidance control strategy are studied.According to the information detected by the detection sensor,the operation and decision-making of the vehicle are controlled to avoid collision with other vehicles.Finally,combined with the safe driving and system dynamics model,the safety time distance model is studied.In order to ensure the safety of driving,the safety distance is taken as one of the indicators of vehicle risk judgment.In order to verify the effectiveness of the deep learning algorithm and the safety distance model,the simulation is carried out based on the co simulation platform of Car Sim and MATLAB / Simulink.According to the results of the simulation experiment,the effectiveness of the safety distance model,collision avoidance strategy and algorithm is proved.In order to further verify the effectiveness of the algorithm and control strategy,a model experiment is carried out.The results of model experiments under different conditions show that the active collision avoidance control system studied in this paper has excellent performance and can achieve the effect of effective collision avoidance in the process of driving. |
