The focus of this paper is on the development of a vehicle-tracking algorithm using a solid-state LIDAR sensor for application to Collision Avoidance Systems (CAS) for Heavy Commercial Road Vehicles. Solid State LIDARs are relatively inexpensive compared to RADARs and point cloud LIDARs, and hence could accelerate commercialization of Advanced Driver Assistance Systems (ADAS) especially in cost-sensitive markets. The suitability of an inexpensive LIDAR sensor for Rear End Collision Avoidance application is analyzed first. Then, using the measurements from the sensor, an Interacting Multiple Model filter and a linear Kalman Filter are used for estimating the longitudinal and the lateral motion variables respectively, for various classes of road vehicles. Good tracking accuracy is achieved in the lateral direction despite the sensor's low angular resolution. The proposed estimation algorithm is first evaluated in a vehicle dynamics software, IPG TruckMaker^®, and then through experiments, and the results are presented.