Advanced occupancy grid techniques for lidar based object detection and tracking

The robust and reliable detection of objects in the surrounding of a vehicle is an important prerequisite for collision avoidance and collision mitigation systems. In this paper, an ego-motion compensated tracking approach is presented which uses extended occupancy grid methods for both detection and tracking of objects observed by lidar. The approach is able to estimate the velocity and direction of moving objects as well as to distinguish between moving and stationary objects. Additionally, an approach for drastically reducing the computational effort is presented.