A unique approach for motion planning for autonomous vehicle using modified Lattice Planner

In order to travel in a chaotic and dynamic environment, an autonomous vehicle requires a motion plan. This motion plan ensures that the vehicle is able to travel without any collision and at the same time also following the traffic rules. The optimum solution for this problem exists in higher dimensions, however, with the help of useful heuristics the problem can be solved in real time, which is required for real time operation of an autonomous vehicle. This is generally known as motion planning for an autonomous vehicle. There are different well established techniques available to plan a collision free kinematically traversable path. One of such techniques is called Lattice planner. However, the legacy version of lattice planner is not optimized and also is prone to fail under specific dynamic environment conditions. Moreover, the legacy version of lattice planner is also not road aware. Due to this reason it a semi optimized way to solve the motion planning problem. This paper discuses a modified version of lattice planner, which uses the road information to better plan the paths by filtering out those paths that are going to end up in a collision. This way, the paths that are obviously going to end up in a collision are pre-eliminated and they do not enter the computationally heavy path-optimization and collision checking cycle. Also the modified lattice planner makes sure that the planned motion paths are more prone to guide the vehicle to a safer trajectory and at the same time following the traffic rules. Lastly, paper discusses the inherent problems with the legacy version of local planner technique and how the modified lattice planner version is able to eliminate these problems. Finally, a one to one comparison is done between the legacy version and the modified version to show the superiority of the modified version over the legacy version.