Research On Vehicle Braking Performance Detection Method Based On Laser Radar

With the rapid development of the automobile manufacturing industry and the economy and society,the number of cars owned has significantly increased,and people are increasingly concerned about traffic safety issues related to it.The braking performance of a car is not only directly related to its driving safety,but also affects its fuel economy and driving smoothness.Therefore,the detection of automotive braking performance is of great significance.At present,the commonly used methods for testing the braking performance of in use vehicles include drum reaction braking test bench and flat plate braking test bench.However,these two detection devices are limited by their own structure and can only detect low-speed vehicles,which cannot effectively evaluate the braking performance of vehicles with ABS systems.The desktop detection method reflects the braking performance of the vehicle through indirect parameters,so the detection results cannot fully reflect the actual braking performance of the vehicle.The current road test detection method requires the installation of complex equipment on vehicles and requires professional personnel to operate,making it difficult to meet the efficiency requirements of vehicle detection.Therefore,it is necessary to propose a method for detecting automotive braking performance based on Li DAR.The article analyzes the evaluation indicators of automotive braking performance and the factors that affect automotive braking performance,and proposes a method based on body feature point extraction to achieve vehicle braking performance detection.By arranging two Li DARs in the detection area,point cloud data is obtained by scanning the vehicle body during braking.Propose a feature point extraction method based on tire profile recognition for vehicle braking trajectory detection;For vehicle motion state detection,analyze the data features of the vehicle contour collected by the radar,fit the vehicle contour curve,and extract the geometric center of the fitted curve as the feature point.To solve the problem of poor clustering effect of different frames of cluster analysis caused by vehicle movement,a DBSCAN clustering algorithm with adaptive threshold is proposed to optimize the clustering effect.The characteristics of the wheels are analyzed and the point cloud data of the tire profile is extracted.The curve fitting of the two tire profile data is performed using the non-linear least squares method.The similarity of the two curves is compared and iterated using the characteristics of tire symmetry,so as to reduce the error caused by vehicle movement.By extracting the centroids of two curves to determine feature points,the braking distance and lateral path deviation of the vehicle are obtained by extracting feature points from different frame point cloud data.Analyze the characteristics of vehicle contour data,extract corner points from the vehicle contour data,and achieve "L" curve fitting.The minimum bounding rectangle and the geometric center of the rectangle are obtained by fitting the curve,and the influence of the shape difference of objects collected in different data frames on the geometric center position is eliminated by coordinate transformation.Using the geometric center as the target tracking feature point for Kalman filtering,a "current" statistical state model is constructed to detect the motion state of the vehicle,obtain the speed change curve during the vehicle braking process,and then obtain the average braking deceleration of the vehicle.Finally,CARLA simulation simulator is used to build a simulation environment for vehicle braking performance testing,Carsim builds a vehicle dynamics model,and verifies this vehicle braking performance testing method through joint simulation.After extracting and processing the feature points of the vehicle body,the braking distance,lateral path deviation,and speed change curve of the vehicle during braking can be obtained.Based on these detection indicators,the braking performance of the vehicle can be evaluated.Finally,compare the error between the results obtained by the detection method in this article and the results output by the simulator to verify the accuracy of the detection method in this article.