Research On Methods In Dynamic Obstacles Detection, Prediction And Avoidance Of Autonomous Vehicles

The autonomous vehicle is one kind of wheeled mobile robots, whose technologies are related to cognitive science, artificial intelligence, robot technology, automotive engineering, and many other interdisciplines. It is the best experimental platform to exam the newly developing technology and an important development direction of today’s advanced science and technology. The autonomous vehicle not only includes the breakthrough of theoretical approaches and key technology, but also, it is related to the issue of engineering and experimentations.Of the unit technology of autonomous vehicles, dynamic obstacles detection, prediction and avoidance in complex outdoor environments is always the emphasis and difficulty of the research. On account of the problem autonomous vehicles encountered in research on dynamic obstacles detection, prediction and collision avoidance in complex outdoor environments, and the general design requirement of the system, a dynamic obstacles avoidance system of autonomous vehicles is presented. In this paper, the methods in detection, prediction and avoidance are researched indepth and some specific techniques of engineering application and experimental testing are introduced. The specific research contents include some aspects as follows.1) The related notion and research results abroad and in China of dynamic obstacles avoidance system of autonomous vehicles are analysed. The basic flow and basic approach of dynamic obstacles detection, prediction and avoidance of autonomous vehicles are understood.2) The system architecture and basic approach of "Intelligent Pioneer" autonomous vehicles are presented. The critical problem of dynamic obstacles detection, prediction and avoidance of the autonomous vehicle in complex outdoor environments is analysed and the general design requirement of the system is proposed. In addition, the "Intelligent Pioneer" dynamic obstacles avoidance system of autonomous vehicles is designed.3) Dynamic obstacle detection approach for the autonomous vehicle is researched. The four layers laser radar "Ibeo" and the three-dimensional laser radar "Velodyne" are used as detection sensors for dynamic obstacles. Their data are respectively processed through a series of processes. Finally, motion information of dynamic obstacles processed from these two types of data is fused. This approach not only meets the demand of accuracy, but also, it meets the demand of real-time of dynamic obstacle detection of autonomous vehicles.4) Dynamic obstacle collision prediction and representation approach for the autonomous vehicle is researched. In this paper, a new occupancy grid named space-time occupancy grid map is presented. It can not only represent the occupancy information of static obstacles, but also, it can represent the predicted collision information of the autonomous vehicle and dynamic obstacles in a period of future time. Different models are used to predict the collision between the autonomous vehicle and dynamic obstacles when the autonomous vehicle travels in different scenarios. The directly generating method and the optimization generating method of space-time occupancy grid map are introduced. This grid map unites the collision avoidance of dynamic obstacles and static obstacles, and simplifies the algorithm complexity of the collision avoidance process.5) Dynamic obstacle avoidance approach for the autonomous vehicle is researched. In this paper, an improved A*algorithm for searching consecutive neighbourhoods is presented. Linear interpolation of the grid is executed. As a result, the searchable neighbourhood number of the conventional A*algorithm is changed from8to infinite and the searchable direction is with an arbitrary and continuous degree. It can solve the problem that the optimal path calculated by the conventional A*algorithm on grid map is not the best one. Then a dynamic and static obstacles collision avoidance approach for the autonomous vehicle based on this improved A*algorithm and the space-time occupancy grid map is introduced. This approach can realize secure and intelligent autonomous driving of the autonomous vehicle in dynamic environments.The above research contents are applied on the "Intelligent Pioneer" autonomous vehicle platforms. The "Intelligent Pioneers" have tested on the actual road and participated in the "Intelligent Vehicle Future Challenge of China" Competition funded by Natural Science Foundation of China. The experimental result and excellent performance in the competition validate the effectiveness and feasibility of this system and the specific methods. At the end of this paper, the advantages and disadvantages of this system are summarized, and an expectation of further research is brought out.