Search Results

In order to avoid the influence of the change of the camera pitch angle and the variation of the height of the ground on the ranging accuracy, improve the real-time performance of the algorithm by substituting the current widely-used monocular vision ranging algorithm which builds the optical model based on the shadow of the vehicle floor and the lane line, as well as avoid the classification of vehicle detection, a vehicle distance measurement algorithm based on monocular vision and license plate width is established. Firstly, the target image acquisition and preprocessing are studied. Then the paper studies the license plate image location segmentation method based on accelerated template matching. On this basis, the algorithm for obtaining the ratio of license plate width to image width is studied, and the function of vehicle distance and license plate ratio width is established.

Vehicle sideslip angle estimation is of great importance to the vehicle stability control as it could not be measured directly by ordinary vehicle-mounted sensors. As a result, researchers worldwide have carried out comprehensive research in estimating the vehicle sideslip angle. First, as the attitude would affect the acceleration information measured by the IMU directly, different kinds of vehicle attitude estimation methods with multi-sensor fusion are presented. Then, the estimation algorithms of the vehicle sideslip angle are classified into the following three aspects: kinematic model based method, dynamic model based method, and fusion method. The characteristics of different estimation algorithms are also discussed. Finally, the conclusion and development trend of the sideslip angle estimation are prospected.

In this paper, a novel method is proposed to establish the vehicle yaw stability criterion based on the sideslip angle-yaw rate (β-r) phase plane method. First, nonlinear two degrees of freedom vehicle analysis model is established by adopting the Magic Formula of nonlinear tire model. Then, according to the model in the Matlab/Simulink environment, the β-r phase plane is gained. Emphatically, the effects of different driving conditions (front wheels steering angle, road adhesion coefficient and speed) on the stability boundaries of the phase plane are analyzed. Through a large number of simulation analysis, results show that there are two types of phase plane: curve stability region and diamond stability region, and the judgment method of the vehicle stability domain type under different driving conditions is solved.

Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance.

Vehicles equipped with one or several functions of Advanced Driver Assistant System (ADAS) and autonomous driving (AD) technology are more mature and prevalent nowadays. Vehicles being smarter and driving being easier is an unstoppable trend. In the near future, intelligent vehicles will be mass produced and running on the road. However, before the mass-production of intelligent vehicles, a lot of experimental tests and validations need to be carried out to insure the safety and reliability of ADAS and AD technology. Although the road test of real vehicles is the most reliable and accurate test method, it cannot meet the need of rapid development of technology research due to high time and financial cost. Therefore, a high-efficient design and evaluation methodology for ADAS and AD development and test is a must. In this paper, a virtual co-simulation platform based on MATLAB/Simulink, OpenModelica and Unity 3D game engine (MOMU) is proposed.