Driverless Vehicle Path Planning Based On Genetic Algorithm Technology Research

In recent years, with the increasing of private cars, the traffic problem is increasingly serious. Driverless cars can not only ease traffic congestion, improve the efficiency of traffic system,it also can greatly improve the safety. As the future development direction of automobile industry, driverless cars technology more and more get the attention of people.This paper main research content is driverless cars of path planning technology.This paper introduces the general situation of the development of driverless cars at home and abroad, and then describes in detail the key technology of driverless cars,the concept of driverless cars path planning and solutions. After comparing the advantages and disadvantages of various path planning methods, finally choose to use genetic algorithm to solve the problem of path planning. Then this paper introduces the development history and characteristics of genetic algorithm, and expounds the main process of genetic algorithm and realization technology.According to the characteristics of the path planning problem, this paper chose a kind of genetic algorithm——Virus genetic algorithm. This paper describes a practical dynamic route planning method using real road maps in a wide area. The maps include traffic signals, road classes, and the number of lanes. The proposed solution is using a genetic algorithm adopting viral infection. The method is to use viruses as domain specific knowledge. A part of an arterial road is regarded as a virus.A population of viruses is generated in addition to a population of routes. Crossover and infection determine the near-optimal combination of viruses. When traffic congestion frequently changes during driving, an alternative route can be selected using viruses and other routes in the population in a real time. At last, this paper use virus genetic algorithm for the simulation experiment results.Finally, this paper uses genetic algorithm respectively on the static and dynamic traffic simulation, discusses the different parameters on the fitness of the results of path planning. The static traffic is divided into simple, secondary and complex roadconditions.Through the study and the experimental results of this paper, it can be fully proved that genetic algorithm can be a very good solution to solve the driverless car path planning problem.