Evidential-Based Approach for Trajectory Planning With Tentacles, for Autonomous Vehicles

Abstract

Driving is a combination of continuous mental risk assessment, sensory awareness, and judgment, all adapting to extremely variable surrounding conditions. Several research projects are working on autonomous vehicle to robotize this complex task. The work presented in this paper focuses on reactive local trajectory planning in uncertain environment. The environment uncertainty is one of the challenges that we face in trajectory planning. For autonomous vehicle, to be efficient, they need to be able to deal with this kind of uncertainty. In this paper, we show that the theory of belief functions with its ability to distinguish between different types of uncertainty is able to provide significant advantages in the context of trajectory planning. Using the belief functions, we build evidential grid that represents the surrounding environment. To plan a local trajectory, we generate a set of clothoid tentacles in the egocentred reference frame related to the ego-vehicle. Those tentacles represent possible trajectories that consider the current dynamical state of the vehicle and make a smooth variation in the vehicle dynamic variables. Once the representation of the environment and the possible trajectories are generated, an evaluation of each trajectory is carried out according to several criteria and the choice of the trajectory is made using the decision formalism of Markov decision process. To demonstrate the effectiveness of our evidential approach, we apply it to scenarios where ego-vehicle has to make decision in uncertain dynamical environments, using a driving simulator (SCANeR™ Studio).