Integrating safety distances with trajectory planning by modifying the occupancy grid for autonomous vehicle navigation

The goal of the work in this paper is to use occupancy grid in integrating safety distances with the planning strategy for autonomous vehicle navigation. The challenge is to avoid static and dynamic obstacles at high speed with respect to some specific road rules while following a global reference trajectory. Our local trajectory planning algorithm is based on the method of clothoid tentacles. It consists on generating clothoid tentacles in the egocentered reference frame related to the vehicle. Using information provided from sensors, we build an occupancy grid that we modify to take into consideration safety distances. We use this modified occupancy grid to classify each tentacle as navigable or not navigable. By formulating the problem as Markov Decision Process, only one tentacle among the navigable ones is chosen as the vehicle local reference trajectory.