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In the field of active safety, the active four-wheel-steering (4WS) system seems to be an attractive alternative and an effective tool to improve the vehicles' handling stability in lane-keeping control performance. Under normal using condition, the vehicle's lateral acceleration is comparatively small, and the mathematic relationship between the small side force excitation and the small slip angle of the tire is in the linear region. Furthermore, the effects of roll, heave, and pitch motions are neglected as well as the dynamic characteristics of the tires and suspension system in this work. Therefore, the linear quadratic control (LQC) theory is used to ensure that the output of the 4WS control system can keep track of the desired yaw rate and zero-sideslip-angle response can also be realized at the same time.

In the intelligent speed planning system, real-time estimation of road slope is the key to calculate slope resistance and realize the vehicles’ active safety control. However, if the road slope is measured by the sensor while the commercial vehicle is driving, the vibration of the vehicle body will affect its measurement accuracy. Therefore, the relevant algorithm is used to estimate the real-time slope of the road when the commercial vehicle is driving. At present, many domestic and foreign scholars have analyzed and tested the estimation of road slope by the least square method or Kalman filter algorithm. Although the two methods both can achieve the estimation, the real-time performance and accuracy still need to be improved. In this paper, for traditional fuel commercial vehicle, the Kalman filter algorithm based on the kinematics and the extended Kalman filter algorithm based on the longitudinal dynamics are respectively used to estimate the road slope.

Vehicle speed is an important factor to driving safety, which is directly related to the stability and braking performance of the vehicle. Besides, the precise measurement of the vehicle speed is the basis of some vehicle active safety systems. Even in the future intelligent transportation, high quality speed information will also play an important role. The commonly used vehicle speed measurement techniques are based on the wheel speed sensors, which are not accurate, especially when the wheels’ slip rate is not equal to zero. Focusing on these issues, image matching technology has been used to measure the vehicle speed in this paper. The image information of the road in the front of the vehicle is collected, and the pixel displacement of the vehicle is calculated by the matching system, thus accurately vehicle speed can be obtained. Compared with conventional speed measure technology, it has the advantages of wide measuring range, and high accuracy.

The heavy-duty vehicles have large transportation capacity. Gross mass and center of gravity position of the heavy-duty vehicles vary with the cargo mass and the driving condition, which affect driving safety and handling stability. Gross mass and center of gravity position of the vehicles are usually measured on fixed test platform, and the vehicles are stationary or pass the platform slowly in the measurement process. Most dynamic weighing system could not measure the center of gravity position of the vehicles. On-board mass and center of gravity of motor vehicles measurement system mainly based on the tire pressure information could measure gross mass and center of gravity position accurately in the driving process. The measurement errors of the sensors are effectively decreased by filtering collected sensor data. The relationship between the tire pressure and the tire load is built when the vehicle is stationary.

Path planning system, which is one of driver assistance systems, can calculate the driving paths and estimate the driving time through the road information provided by information source. Traditional path planning systems calculate the driving paths through Dijsktra's algorithm or A* algorithm but only consider the road information from electronic maps. It is not safe enough for operating vehicles because of the insufficient information of vehicle performance as well as the driver's willingness. This study is based on the Dijsktra's algorithm, which comprehensively considered vehicular active safety constraints such as road information, vehicle performance and the driver's willingness to optimize the Dijsktra's algorithm. Then the path planning system can calculate the optimal driving paths that would satisfy the safety requirement of the vehicle. This study used LabVIEW as a visual host computer and MATLAB to calculate dynamic property of the vehicle.

H-Bahn ("hanging railway") refers to the suspended, unmanned urban railway transportation system. Through the reasonable platform layout, H-Bahn can be easily integrated into the existing urban transit system. With the development of urban roads, the associated rail facilities can be conveniently disassembled, moved and expanded. The track beam, circuits, communication equipment, and sound insulation screen are all installed in a box-type track beam so that the system can achieve a high level of integration and intelligence. The carriage of the modern H-banh vehicle is connected with the bogies by two hanging devices. The vehicle is always running in the box-type track beam; therefore there are less possibilities of derailment. Consequently, the key work focuses on the running stability evaluation and curve negotiation performance analysis.

Dynamic modeling and state estimation are significant in the trajectory tracking and stability control of the intelligent vehicle. In order to meet the requirement of the stability control of the eight-in-wheel-motor-driven intelligent vehicle, a full vehicle dynamics model with 12 degrees of freedom, including the longitudinal, lateral, yaw and roll motion of the body, and rotational motion of 8 wheels, is established for the research of the intelligent vehicle in this paper. By simulation with MATLAB/SIMULINK and by comparison with the TruckSim software, the reliability and practicality of the dynamics model are verified. Based on the established dynamics model, an extended Kalman filter (EKF) state observer is proposed to estimate the vehicle sideslip angle, roll angle and yaw rate, which are the key parameters to the stability control of the intelligent vehicle.

Downhill mountain roads are the accident prone sections because of their complexity and variety. Drivers rely more on driving experience and it is very easy to cause traffic accidents due to the negligence or the judgment failure. Traditional active safety systems, such as ABS, having subjecting to the driver's visual feedback, can’t fully guarantee the downhill driving safety in complex terrain environments. To enhance the safety of vehicles in the downhill, this study combines the characteristics of vehicle dynamics and the geographic information. Thus, through which the drivers could obtain the safety speed specified for his/her vehicle in the given downhill terrains and operate in advance to reduce traffic accidents due to driver's judgment failure and avoid the brake overheating and enhance the safety of vehicles in the downhill.

This paper presents a novel application of hydraulic electromagnetic energy-regenerative shock absorber (HESA) into commercial vehicle suspension system and vehicle road performance are simulated by the evaluating indexes (e.g. root-mean-square values of vertical acceleration of sprung mass, dynamic tire-ground contact force, suspension deflection and harvested power; maximum values of pitch angle and roll angle). Firstly, the configuration and working principle of HESA are introduced. Then, the damping characteristics of HESA and the seven-degrees-of-freedom vehicle dynamics were modeled respectively before deriving the dynamic characteristics of a vehicle equipped with HESA. The control current is fixed at 7A to match the similar damping effect of traditional damper on the basis of energy conversion method of nonlinear shock absorber.

The tank truck has a wide range of application. When the liquid in the tank is not fully loaded, the lateral movement of the liquid in the tank will shift the center of gravity of the tank truck and make the vehicle less safe. It is easy to roll over when the tank truck is turning. This study combines the vehicle dynamic characteristics and geographic information, which gives the driver safe speed and safe braking distance tips before turning, to reduce the traffic accidents caused by driver's misjudgment. The dynamic model of the tank truck is established, through collecting the real-time information of the vehicle, the vehicle load and braking torque are calculated by the relevant dynamic model. The system needs to measure the deviation of the center of gravity in the tank truck movement process, and the deviation of the center of gravity has a great influence on the safety speed.

The brake performance is one of the most important performances in the automotive active safety, and it is the main measure of automotive active safety. Thus, to develop a platform for the braking system is quite significant. Based on the object-oriented technology, the platform for braking system is developed by making use of Visual C++ 6.0 development tool. By using the VC++ development tool and doing secondary development on other softwares, the software possesses powerful features, such as brake plan selection, performance calculation, parametric modeling, finite element analysis and kinematics simulation, etc. An initial brake system can be designed, calculated and analyzed all in one. The living instance shows that the platform has friendly user interfaces, powerful functions and it can improve the precision and efficiency of brake design. The platform has been of great applied value and can also positively promote the design automation of vehicle's braking system.

With the rapid development of the logistics and transportation industry, heavy-duty trucks play an increasingly important role in social life. However, due to the characteristics of large cargo loads, high center of mass and relatively narrow wheelbase, the driving stability of heavy trucks are poor, and it is easy to cause rollover accidents under high-speed driving conditions, large angle steering and emergency obstacle avoidance. To improve the roll stability of heavy trucks, it is necessary to design an active anti-rollover control system, through the analysis of the yaw rate and the load transfer rate of the vehicle, driving states can be estimated during the driving process. Under the intervention of the control system, the lateral transfer rate of heavy trucks can be reduced to correct the driving posture of the vehicle body and reduce the possibility of rollover accidents.

Preventing skidding is essential for studying the safety of driving in curves. However, the adhesion of the vehicle during the driving process on the wet and slippery road will be significantly reduced, resulting in lateral slippage due to the low adhesion coefficient of the road surface, the speed exceeding the turning critical, and the turning radius being too small when passing through the corner. Therefore, to reduce the incidence of traffic accidents of passenger cars driving in curves on rainy and snowy days and achieve the purpose of planning safe driving speed, this paper proposes a curve active safety system based on a deep learning algorithm and vehicle dynamics model. First,we a convolutional neural network (CNN) model is constructed to extract and judge the characteristics of snow and ice adhesion on roads.

The automatic emergency braking (AEB) system is an important part of automobile active safety, which can effectively reduce rear-end collision accidents and protect drivers' safety through active braking. AEB system has been included in many countries' new car assessment programme as the test content of active safety. In view of obviously deficiencies of the existing AEB control algorithm in avoiding longitudinal collision at high speed, it is proposed to an optimized model of the minimum safe distance for rear-end collision prevention on high-speed road in order to improve the accuracy of AEB system. Considering the influence of road adhesion coefficient and human comfort on the maximum braking deceleration, it is established to a more accurate and reasonable AEB system to avoid collision for expressway. The collision avoidance strategy is verified by simulation software.

Before the new car rolls from the line, due to assembly errors, inaccurate four-wheel positioning, etc., the vehicle will run off on a flat road, which will affect the driving comfort and safety. At present, most automobile manufacturers choose to perform the deviation test in the process of vehicle rolls from the line. Compared with other detection methods, the online deviation test system is developed with high precision of laser ranging, fast response and good reliability, which can realize fast and high-precision detection of vehicle deviation. In this paper, test system software is developed based on the LABVIEW, a variety of communication methods to build the communication system, using information check and queue task processing to control, to meet the test needs. Firstly, the calculation model of the deviation of the test system is established.

Commercial vehicle plays an important role during transportation process under the demand of high speed, convenience and efficiency. So improving active safety of commercial vehicle has become a research topic. Due to the fact that braking characteristic is the basic and most closely related to safe driving of vehicle's performances, this paper aims to improve the braking performance by researching into an integrated control method based on the mature ABS products. Firstly, a strategy which gives priority to ABS and differential yaw moment control, complementary with the hydraulic active suspension control is proposed. In comparison with ABS, the combined control of brake system and suspension system is designed not only for preventing wheels lock. But the directional control to avoid roll or spin is more focused on. Then in order to run the novel method correctly, the controlled variables and evaluation criteria are illustrated briefly.

As an important input parameter of intelligent vehicle active safety technology, road adhesion coefficient is of great significance in autonomous collision avoidance, emergency braking and collision avoidance, and variable adhesion road motion control. Traditional recognition methods based on vehicle dynamics require large data volume and low solution accuracy. This paper proposes an adhesion coefficient recognition method based on Elman neural network and Kalman filter. By establishing a seven-degree-of-freedom vehicle dynamics model, dynamic parameters such as yaw angular velocity, longitudinal velocity, lateral velocity, and angular velocity of each wheel, which are easy to measure and strongly related to the road adhesion coefficient, are analyzed as the input of the neural network model.

Compared with urban areas, the road surface in mountainous areas generally has a larger slope, larger curvature and narrower width, and the vehicle may roll over and other dangers on such a road. In the case of limited driver information, if the two cars on the mountain road approach fast, it is very likely to occur road blockage or even collision. Multi-vehicle cooperative control technology can integrate the driving data of nearby vehicles, expand the perception range of vehicles, assist driving through multi-objective optimization algorithm, and improve the driving safety and traffic system reliability. Most existing studies on cooperative control of multiple vehicles is mainly focused on urban areas with stable environment, while ignoring complex conditions in mountainous areas and the influence of driver status. In this study, a digital twin based multi-vehicle cooperative warning system was proposed to improve the safety of multiple vehicles on mountain roads.