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Under extreme working conditions such as high-speed driving on roads with a large road surface unevenness coefficient, turning on a road with a low road surface adhesion coefficient, and emergency acceleration and braking, a vehicle’s stability deteriorates sharply and reduces ride comfort. There is extensive existing research on vehicle active suspension control, trajectory tracking, and control methods. However, most of these studies focus on conventional operating conditions, while vehicle stability analysis under extreme operating conditions is much less studied. In order to improve the stability of the whole vehicle under extreme operating conditions, this paper investigates the stability of a vehicle under extreme operating conditions based on linear quadratic regulator (LQR) control. First, a seven degrees of freedom (7-DOF) dynamics model of the whole vehicle is established based on the use of electromagnetic active suspension, and then an LQR controller of the electromagnetic active suspension is designed. A joint simulation platform incorporating MATLAB and CarSim was built, and the CarSim model is verified by real vehicle tests. Finally, the stability of the vehicle under four different ultimate operating conditions was analyzed. The simulation results show that the root mean square (RMS) values of body droop acceleration and pitch angle acceleration are improved by 57.48% and 28.81%, respectively, under high-speed driving conditions on Class C roads. Under the double-shift condition with a low adhesion coefficient, the RMS values of body droop acceleration, pitch acceleration, and roll angle acceleration are improved by 58.25%, 55.41%, and 31.39%, respectively. These results indicate that electromagnetic active suspension can significantly improve vehicle stability and reduce driving risk under extreme working conditions when combined with an LQR controller.

The AEB-P (Autonomous Emergency Braking Pedestrian) system has the functional requirements of avoiding the pedestrian collision and ensuring the pedestrian’s life safety. By studying relevant theoretical systems, such as TTC (time to collision) and braking safety distance, an AEB-P warning model was established, and the traffic safety level and work area of the AEB-P warning system were defined. The upper-layer fuzzy neural network controller of the AEB-P system was designed, and the BP (backpropagation) neural network was trained by collected pedestrian longitudinal anti-collision braking operation data of experienced drivers. Also, the fuzzy neural network model was optimized by introducing the genetic algorithm. The lower-layer controller of the AEB-P system was designed based on the PID (proportional integral derivative controller) theory, which realizes the conversion of the expected speed reduction to the pressure of a vehicle braking pipeline. The relevant pedestrian test scenarios were set up based on the C-NCAP (China-new car assessment program) test standards. The CarSim and Simulink co-simulation model of the AEB-P system was established, and a multi-condition simulation analysis was performed. The results showed that the proposed control strategy was credible and reliable and could flexibly allocate early warning and braking time according to the change in actual working conditions, to reduce the occurrence of pedestrian collision accidents.

This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M1 and N1 vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle’s maximum mass, which often results in underperformance during inspections due to vehicles typically being unladen. This discrepancy arises because the maximum braking forces are proportional to the vertical load on the wheels, causing empty vehicles to lock their wheels prematurely compared to laden ones. fBrake simulates the braking forces of unladen vehicles to reflect a laden state by employing an optimal brake-force distribution curve that aligns with the vehicle’s inherent braking behavior, whether through proportioning valves or through electronic brake distribution systems in anti-lock-braking-system-equipped vehicles. Our methodology, previously applied to heavy vehicles, involved extensive experimentation with a roller brake tester, comparing the actual braking performances of dozens of vehicles to those of their simulated counterparts using fBrake. The results demonstrate that fBrake reliably replicates the braking efficiency of laden vehicles, validating its use as an accurate and effective tool for braking system assessments in periodic inspections, irrespective of the vehicle’s load condition during the test. This approach ensures compliance with regulatory requirements while enhancing the reliability and safety of vehicle inspections.

A trustworthy index that accurately displays real‐time rollover danger during vehicle maneuvers in order to design a rollover prevention system is critical to have. In this present work, the rollover of four‐wheeled vehicles on banked curves is investigated. The mathematical model is the vehicle’s rollover index on a banked curve, which is based on the lateral load transfer ratio (LTR) principle. The LTR takes the track width, location of the center of gravity, and vehicle mass as major vehicle parameters and also longitudinal velocity, lateral acceleration, road radius, steering angle, and bank angle as major state variables. The sensitivity analysis showed rollover is more sensitive to track width with a sensitivity coefficient of 1 and then to forward velocity with a sensitivity coefficient of 0.89. Distance between CGs, lateral acceleration, turning radius, and distance of CG from roll center have also a sensitivity coefficient of 0.81, 0.7, 0.69, and 0.63, respectively.

This research focuses on controlling the motion trajectory of autonomous vehicles by using a combination of two high-performance control methods: Linear Parameter Varying (LPV) and Reinforcement Learning (RL). First, a single-track motion model is researched and developed with coordinate systems to determine the car's motion trajectory through signals from GPS. Then, the LPV control method is used to design a controller to control the car's motion trajectory. Reinforcement learning method with detailed training procedures is used to combine with the advantages of LPV controller. Finally, the simulation results are evaluated in the time domain through the use of specialized CarSim software, which clearly demonstrates the superiority of the research method.

In this paper an active controller for ground vehicles stability is presented. The objective of this controller is to force the vehicle to track a desired reference, ensuring safe driving conditions in the case of adhesion loss during hazardous maneuvers. To this aim, a nonlinear discrete-time inverse optimal control based on a neural network identification is designed, using a recurrent high order neural network (RHONN) trained by an Extended Kalman Filter. The RHONN ensures stability of the identification error, while the controller ensures the stability of the tracking errors. Moreover, a discrete-time reduced order state observer is utilized to reconstruct the lateral vehicle dynamic not usually available. For the control problem, the references of the lateral velocity and yaw rate are given by a dynamic system mimicking an ideal vehicle having not-decreasing tire lateral characteristics. The proposed approach avoids the identification of the Pacejka’s lateral parameters of the tires, so simplifying the input control determination. Moreover, an optimal control is proposed to optimize the actuator effort and power, usually bounded. Control gains are determined using optimal “nature-inspired\" algorithms such as particle swarm optimization. Test maneuvers, performed through the full vehicle simulator CarSim ® , have been used to test correctness, quality and performances of the observer, the neural identifier and the inverse optimal controller. Robustness of the reduced order discrete-time state observer is also discussed for different sample times. Finally, a fair comparison between optimal and non-optimal control schemes is presented, highlighting the numerical results obtained in simulation.

In recent years, since the unique advantages in automotive structures, the vehicle active suspension systems have received widespread attentions. A good active suspension system can reduce the vibration and improve the overall performance of the vehicle. Therefore, the design of the controller for the active suspension system to perform autonomous adjustment plays a vital role in vehicle comfort and safety. For the active suspension of the seven-DOF sport utility vehicle (SUV) model, this paper takes the vehicle body acceleration, tire dynamic load and suspension dynamic travel as the indicators to evaluate the performance, and the proportional-integral-derivative (PID) controller is designed to improve the performance of the vehicle active suspension system. Based on the software of MATLAB/Simulink and Carsim, a closed-loop co-simulation model diagram is established, which includes a PID controller module. Meanwhile, the random road input model and the whole vehicle model are constructed in Carsim. Finally, at the speeds of 70, 90, and 120 km/h, the active suspension system under the designed PID controller is simulated and compared with the passive suspension system. The simulation results show that the active suspension system based on PID controller can effectively improve the overall performance of the vehicle, and then the comfort and safety of the vehicle can be further enhanced.

An intelligent vehicle must face a wide variety of situations ranging from safe and comfortable to more aggressive ones. Smooth maneuvers are adequately addressed by means of linear control, whereas more aggressive maneuvers are tackled by nonlinear techniques. Likewise, there exist intermediate scenarios where the required responses are smooth but constrained in some way (rise time, settling time, overshoot). Due to the existence of the fundamental linear limitations, which impose restrictions on the attainable time-domain and frequency-domain performance, linear systems cannot provide smoothness while operating in compliance with the previous restrictions. For this reason, this article aims to explore the effects of reset control on the alleviation of these limitations for a lane change maneuver under a set of demanding design conditions to guarantee a suitable ride quality and a swift response. To this end, several reset strategies are considered, determining the best reset condition to apply as well as the magnitude thereto. Concerning the reset condition that triggers the reset action, three strategies are considered: zero crossing of the controller input, fixed reset band and variable reset band. As far as the magnitude of the reset action is concerned, a full-reset technique is compared to a Lyapunov-based error minimization method to calculate the optimal reset percentage. The base linear controller subject to the reset action is searched via genetic algorithms. The proposed controllers are validated by means of CarSim.

This study explores the detailed effects of speed, road adhesion, road slope, and dynamic maneuvers on the lateral stability of vehicles equipped with four‐wheel steering (4WS) systems, also referred to as all‐wheel steering (AWS) system. The research uses CarSim simulation software to comprehensively evaluate vehicle performance under various speeds (30–90 km/h), road slope (10°–30°) and adhesion coefficients (0.1–0.85) during lane changes and turning maneuvers. The results obtained reveal that 4WS significantly enhances stability and handling at moderate speeds and favorable adhesion conditions. However, high‐speed operations, particularly on low adhesion surfaces or with a steep road slope, increase instability and safety risks. Practical implications also emphasize the importance of maintaining safe speeds, particularly under adverse road conditions, and decreasing speed during sharp turns to counteract centrifugal forces. Finally, these findings highlight the substantial role of cautious driving and adherence to speed limits in improving overall safety for 4WS vehicles. This study thoroughly analyzes and evaluates the detailed effects of speed and road conditions on the directional stability of four‐wheel steering (4WS) vehicles under various driving scenarios in CarSim simulations. The findings highlight the 4WS advantages under moderate conditions and stress cautious driving on slippery or inclined roads for safety.

Aiming to address the issue of highway traffic safety under crosswind conditions, this study utilizes the CarSim/TruckSim simulation platform to systematically analyze the effects of crosswind speed and direction on the driving stability of cars and trucks. A safety speed model is developed for different road adhesion coefficients, and a highway crosswind warning system is designed. Through 625 simulation experiments, the study reveals that lateral offset, lateral acceleration, and lateral load transfer rate are significantly influenced by vehicle speed, wind speed, wind direction, and road adhesion coefficient, with the road adhesion coefficient identified as the key factor. Separate safety speed models for cars and trucks under various road and crosswind conditions are established. The findings are as follows: for cars, crosswind speed and direction impact safe driving speed only when the road adhesion coefficient is 0.1. Overall, for constant wind direction, safe driving speed decreases as wind speed increases; at a constant wind speed, safe driving speed gradually decreases as wind direction shifts from 45° to 135°. For trucks, when the road adhesion coefficient ranges from 0.1 to 0.9, the relationship between safe driving speed, wind speed, and wind direction mirrors that of small cars. However, the critical safety speed for trucks is 40% lower than that for cars under identical crosswind conditions when the road adhesion coefficient is 0.1. Based on the Visual FoxPro platform, which enables real‐time early warning decision‐making through the integration of the safety speed model, the highway driving stability early warning system (comprising information collection, processing, and release modules) is applied to the Zhengzhou Taohuayu Yellow River Highway Bridge case. The system is verified to significantly enhance highway driving safety and provides technical support for dynamic safety management and control of highways under crosswind conditions.