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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.

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 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.

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.

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.

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.

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.

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.

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.

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.