Intelligent Collection of Detection Data and Automatic Generation of Report Based on LabVIEW in ABS Adhesion Coefficient Utilization Test

The automobile anti-lock braking system (ABS) is a hot spot in the current research and development of automobiles. The country also has corresponding standards for its performance requirements and test methods. Among them, the utilization rate of the adhesion coefficient is one of its important performance evaluation indicators. In the adhesion coefficient utilization test, the post-processing workload of the data is large, and the authenticity of the data is crucial to the test results. In order to overcome the shortcomings of manual reporting, which takes a long time and is prone to human errors. Based on the LabVIEW platform, this paper constructs a method for automatic generation of test reports, and designs a comprehensive program for automatic report generation. Experimental shows that the use of integrated automation software to issue an adhesion coefficient utilization test report can reduce data post-processing work and greatly improve the efficiency and accuracy of the report.

Pavement State Recognition Method Considering Slope

For the problem of road surface condition recognition, this paper proposes a real-time tracking method to estimate road surface slope and adhesion coefficient. Based on the fusion of dynamics and kinematics, the current road slope of the vehicle which correct vertical load is estimated. The effect of the noise from dynamic and kinematic methods on the estimation results is removed by designing a filter. The normalized longitudinal force and lateral force are calculated by Dugoff tire model, and the Jacobian matrix of the vector function of the process equation is obtained by combining the relevant theory of EKF algorithm. The road adhesion coefficient is estimated finally. The effectiveness of the algorithm is demonstrated by analyzing the results under different operating conditions, such as docking road and bisectional road, using a joint simulation of Matlab/Simulink and Carsim.

Research on Shimmy Characteristics and Control Strategy of Double-Axle Steering Mechanism with Multiple Limit Cycles Considering Vehicle-Road Coupling

The vehicle and the road are a coupled system, the road surface characteristics have a great influence on the vehicle's handling stability and driving safety. So in this study, A 9-DOF mathematical model of dual-axle steering mechanism considering the vehicle-road coupling is established. Taking one multi-axle vehicle with dual-axle steering mechanism for example, the numerical calculation method is used to reproduce the multiple limit-cycles shimmy, and the effects of the road adhesion coefficient on the multiple limit-cycles shimmy characteristics is analyzed. The results show that the maximum swing angles of first and second axle and the speed ranges of multiple limit-cycles shimmy occurring decrease with the road adhesion coefficient decreasing, and as the road adhesion coefficient decreases, the speed range of multiple limit-cycles occurring tend to shrink to the middle speed range(40-50km/h) which is multi-axle vehicles often travelling. In addition, we also found that the multiple limit-cycles shimmy characteristic of the dual-axle steering mechanism is more sensitive to the change of the road adhesion coefficient of second axle than the change of the road adhesion coefficient of first axle. This finding can provide theoretical guidance for suppressing multiple limit-cycles shimmy of multi-axle vehicle with dual-axle steering mechanism. Finally, we propose a control strategy for the multiple limit cycle shimmy phenomenon, which reduces the amplitude of the shimmy and the interval of multiple limit cycles.

Improved AEB algorithm combined with estimating the adhesion coefficient of road ahead and considering the performance of EHB

This paper proposes an improved autonomous emergency braking (AEB) algorithm intended for intelligent vehicle. Featuring a combination with the estimation of road adhesion coefficient, the proposed approach takes into account the performance of electronic hydraulic brake. In order for the accurate yet fast estimate of road ahead adhesion coefficient, the expectation maximization framework is applied depending on the reflectivity of ground extracted by multiple beams lidar in four major steps, which are the rough extraction of ground points based on 3 σ criterion, the accurate extraction of ground points through principal component analysis (PCA), the main distribution characteristics of ground as extracted using the expectation maximum method (EM) and the estimation of road adhesion coefficient via joint probability. In order to describe the performance of EHB, the response characteristics, as well as the forward and adverse models of both braking pressure and acceleration are obtained. Then, with two typical roads including single homogeneous road and fragment pavement, the safe distance of improved AEB is modeled. To validate the algorithm developed in this paper, various tests have been conducted. According to the test results, the reflectivity of laser point cloud is effective in estimating the road adhesion coefficient. Moreover, considering the performance of EHB system, the improved AEB algorithm is deemed more consistent with the practicalities.

Research on intelligent vehicle lane changing and obstacle avoidance control based on road adhesion coefficient

Aimed at the safety and stability problems of intelligent vehicles under extreme conditions such as low adhesion road surface and emergency lane change and obstacle avoidance, this article designs a lane change and obstacle avoidance controller based on road adhesion coefficient. Using the nonlinear vehicle dynamics model as the prediction model, using the recursive least squares method to identify the road adhesion coefficient, considering the road adhesion coefficient to plan and adjust in the obstacle avoidance path as well as limit constraint conditions of the model predictive control controller, using model predictive control method for the expectation of intelligent vehicle trajectory tracking, travels tremendously guarantee the security and stability of driving. The joint CarSim–Simulink simulations results show that under poor road conditions, the trajectory tracking accuracy after optimization is higher and the vehicle is less prone to sideslip and instability. The lane change controller designed in this article has strong adaptability to different road surface adhesion coefficient, and all parameters can be controlled within a reasonable safety range at different speeds, with good robustness.

Driving Safety Analysis Using Grid-Based Water-Filled Rut Depth Distribution

The water accumulated in the rutted road sections poses a threat to the safety of vehicles. Water-filled ruts will cause partial or complete loss of the friction between tires and the road surface, leading to driving safety hazards such as hydroplaning and sliding. At present, the maximum water depth of left and right ruts is mostly adopted to analyze the safety of water-filled ruts, ignoring the uneven change of ruts in the driving direction and the cross-section direction, which cannot fully reflect the actual impact of asymmetric or uneven longitudinal ruts on the vehicle. In order to explore the impact of water-filled ruts on driving safety, a three-dimensional (3D) tire-road finite element model is established in this paper to calculate the adhesion coefficient between the tire and the road surface. Moreover, a model of the 3D water-filled rut-adhesion coefficient vehicle is established and simulated by the dynamics software CarSim. In addition, the influence of the water depth difference between the left and right ruts on the driving safety is quantitatively analyzed, and a safety prediction model for the water-filled rut is established. The results of the case study show that (1) the length of dangerous road sections based on vehicle skidding is longer than that based on hydroplaning, and the length of dangerous road sections based on hydroplaning is underestimated by 9.4%–100%; (2) as the vehicle speed drops from 120 km/h to 80 km/h, the length of dangerous road sections obtained based on vehicle sliding analysis is reduced by 93.8%. Therefore, in order to ensure driving safety, the speed limit is controlled within 80 km/h to ensure that the vehicle will not skid. The proposed method provides a good foundation for the vehicles to actively respond to the situation of the water-filled road section.

Autonomous vehicle path tracking control considering the stability under lane change

Considering that when a vehicle travels on a low friction coefficient road with high speed, the path tracking ability declines. To keep the performance of path tracking and improve the stabilization under that situation, this article presents approaches to estimate the parameters and control the vehicle. First, the key states of the vehicle and the road adhesion coefficient are estimated by the unscented Kalman filter. This is followed by applying the linear time-varying model-based predictive controller to achieve path tracking control, and the initial tire steering angle control rate is obtained. Finally, the steering angle compensation controller is simultaneously designed by a simple receding horizon corrector algorithm to improve vehicle stability when the path is tracked on a low-adhesion coefficient or at high speed. The performance of the proposed approach is evaluated by software CarSim and MATLAB/Simulink. Simulation results show that an improvement in the performance of path tracking and stabilization can be achieved by the integrated controller under the variable road adhesion coefficient condition and high speed with 110 km/h.