Search Results

LiDAR sensors have played more and more important role on Intelligent and Connected Vehicles (ICV) and Advanced Driver Assistance Systems (ADAS) .However, the development and testing of LiDAR sensors under real driving environment for ADAS applications are greatly limited by various factors, and often are impossible due to safety concerns. This paper proposed a novel functional LiDAR model under virtual driving environment to support development of LiDAR-based ADAS applications under early stage. Unlike traditional approaches on LiDAR sensor modeling, the proposed method includes both geometrical modeling approach and physical modeling approach. While geometric model mainly produces ideal scanning results based on computer graphics, the physical model further brings physical influences on top of the geometric model. The range detection is derived and optimized based on its physical detection and measurement mechanism.

With the development of the advanced driver assistance system and autonomous vehicle techniques, a precise description of the driver’s steering behavior with mathematical models has attracted a great attention. However, the driver’s steering maneuver demonstrates the stochastic characteristic due to a series of complex and uncertain factors, such as the weather, road, and driver’s physiological and psychological limits, generating negative effects on the performance of the vehicle or the driver assistance system. Hence, this paper explores the stochastic characteristic of driver’s steering behavior and a novel steering controller considering this stochastic characteristic is proposed based on stochastic model predictive control (SMPC). Firstly, a search algorithm is derived to describe the driver’s road preview behavior.

Nowadays active collision avoidance has become a major focus of research, and a variety of detection and tracking methods of obstacles in front of host vehicle have been applied to it. In this paper, laser radars are chosen as sensors to obtain relevant information, after which an algorithm used to detect and track vehicles in front is provided. The algorithm determines radar's ROI (Region of Interest), then uses a laser radar to scan the 2D space so as to obtain the information of the position and the distance of the targets which could be determined as obstacles. The information obtained will be filtered and then be transformed into cartesian coordinates, after that the coordinate point will be clustered so that the profile of the targets can be determined. A threshold will be set to judge whether the targets are obstacles or not. Last Kalman filter will be used for target tracking. To verify the presented algorithm, related experiments have been designed and carried out.