Multimodal sparse LIDAR object tracking in clutter

one of the key components of the perception system in an autonomous vehicle or ADAS is the target tracking module. Using target tracking in the sea of clutter, self-driving cars are able to better understand the environment and make predictions about the surrounding objects. Cuboids obtained from a sparse LIDAR often exhibit a fluctuating behavior due to segmentation problems and errors accumulated from the motion correction module. Furthermore, targets in real life scenarios do not move in a predictable manner, so it is very difficult for a classical motion model to describe the complex behavior of any road objects in such cases. In this paper we propose a two-step data association scheme that efficiently and effectively finds correspondences between tracks and measurements. Then we aim to generate better position estimates for objects with an ambiguous dynamic behavior by associating and combining the results from two different motion models. The proposed solution runs in real time and it was validated using a high precision GPS, and also by projecting the prediction results in the corresponding intensity image and assessing whether the prediction falls on the correct item.