  Detecting surrounding vehicles by low-cost LIDAR has been drawing enormous
attention. In low-cost LIDAR, vehicles present a multi-layer L-Shape. Based on
our previous optimization/criteria-based L-Shape fitting algorithm, we here
propose a data-driven and model-based method for robust vehicle segmentation
and tracking. The new method uses T-linkage RANSAC to take a limited amount of
noisy data and performs a robust segmentation for a moving car against noise.
Compared with our previous method, T-Linkage RANSAC is more tolerant of
observation uncertainties, i.e., the number of sides of the target being
observed, and gets rid of the L-Shape assumption. In addition, a vehicle
tracking system with Multi-Model Association (MMA) is built upon the
segmentation result, which provides smooth trajectories of tracked objects. A
manually labeled dataset from low-cost multi-layer LIDARs for validation will
also be released with the paper. Experiments on the dataset show that the new
approach outperforms previous ones based on multiple criteria. The new
algorithm can also run in real-time.
