An Improved Local Dynamic Path Planning Algorithm for Autonomous Driving In this paper, an improved autonomous driving local dynamic path planning algorithm is proposed. Based on the predefined road center points, a set of path control points is constructed, and a onedimensional cubic equation is used to fit the path and construct a center line. A new curved coordinate system is provided using the center line, and the path candidates are generated by arc length and lateral offset. The overall path is selected in consideration of the total cost of path safety and comfort. The results showed that under different scenarios, the proposed local path planning algorithm can plan an optimal path that does not collide with static obstacles, and can ensure the comfort of autonomous driving vehicles and the realtime path planning.

Development of a Navigation Algorithm for Optimal Path Planning for Autonomous Electric Vehicles There are many different driver assistance systems that help the driver controlling their vehicle. These systems do not have full control of the car; however, there are more and more systems in development that take over individual driving functions completely. We are working towards a fully autonomous future. The goal is not only to transport people to their desired destination, but to make commuting on roads safer. Navigation is one of the essential tasks of autonomous driving. The software must independently calculate a route from its current location to the destination. If the vehicle is not guided precisely through the road network, it can never reach that destination. To cope with the problem, the data of the road network must be available to the vehicle. Based on this data, the vehicle must be able to calculate a route to its destination. This paper deals with the development of a navigation algorithm, for optimal path planning, within the framework of an industrial project. The various requirements for the realization of a navigation system are listed. Solution strategies of such systems are identified and compared. Based on this, approaches for solving the task are developed and implemented.