Multirate Obstacle Tracking and Path Planning for Intelligent Vehicles

This paper introduces a new approach for tracking and path planning for intelligent vehicles. The tracking application takes into account the trajectories followed by the obstacles, making a prediction of their future positions and corresponding uncertainties. This idea introduces a stochastic model for obstacle and vehicle kinematics. A multi-rate Kalman filter is considered in the tracking process in order to manage the uncertainty. Potential Field approach for path planning is redefined according to the new stochastic models. In particular, the repulsive potential field is modified to consider these models projected into a prediction horizon. The use of future information minimizes the risk of collisions and generates smoother trajectories.