Research On ITS Collision Avoidance Technology Under Complex Road Conditions

With the rapid development of the national economy and the acceleration of urbanization,the number of motor vehicles in China has increased dramatically,and the problem of traffic accidents has been increasing.For urban traffic accidents,most of them are caused by collisions with vehicles which caused serious economic losses.Therefore,in order to solve the problem of vehicle collision,plenty of theoretical deductions and experimental researches have been studied for ITS(Intelligent Transportation System)anti-collision technology under complex road conditions,and a set of solutions of real-time and reliable vehicle anti-collision have been proposed.It is indispensable to design a system with reliable collision detection and danger avoidance for ITS.The method of vehicle collision avoidance is consistent of three function modules: the vehicle detection model,the vehicle tracking model,and the anti-collision judgment model.The signal used is a general-purpose vehicle radar signal.The radar signal is input to a detector for vehicle detection,which can detect whether there is a vehicle in front,and the detection distance and detection probability can be calculated.If there is a car in front,the vehicle can be targeted in real time with Kalman filter algorithm by observing the status information of the vehicle in front,to predict the trajectory of the vehicle and obtain the predicted driving model of the preceding vehicle.And the prediction model should be input into the anti-collision judgment model to check whether the rear car should brake.This thesis mainly contains the following work.First,by demonstrating that the GLRT-based spatio-temporal correlation detector performs well,especially when the detector based on frequency domain GLRT performs best,it can accurately detect the vehicle in front and provide the driver with a far distance.More operational time.Second,the vehicle’s detection and tracking are combined through the GLRT detector and the Kalman filter.Second,the vehicle can be targeted in real time with Kalman filter algorithm by observing the status information of the vehicle in front,to predict the trajectory of the vehicle and obtain the predicted driving model of the preceding vehicle.And an ITS anti-collision scheme under complex road conditions is proposed combining the anti-crash decision model.Compared with other anti-collision method,this method proposed in this thesis uses a signal detector with excellent performance.In the case of a complex road environment with large ambient noise,it can still accurately detect the preceding vehicle,increase the detection distance,and improve the detection probability.Then,the Kalman filter is used in the anti-collision method to target vehicle in real time,and the track of the target vehicle can be predicted to obtain the prediction driving model of the target vehicle.The corresponding braking method in real time is taken according to the change of the target vehicle’s prediction driving model.It is available to the different situation where the real driving model of the vehicle is constantly changing,and it can be used without finding the real driving model of front vehicle.Finally,it is fully considered with the real scene in the traffic for the thesis,and the turning of vehicle is introduced which can make the experimental perform more authentically.Based on simulation experiments proved,the ITS anti-collision method under the complex road conditions proposed is available which can improve the safety of the driving process and can effectively avoid vehicle collisions.