Vehicle tracking problem is of crucial importance in intelligent vehicles research, as it is amongst the basic components of any comprehensive situation awareness technology. In mixed-traffic environments, where vehicles with varying degrees of sensing and communication capabilities coexist, the vehicle-tracking problem becomes particularly more demanding. In this paper, a collaborative vehicle tracking approach is presented, where onboard sensing and inter-vehicular communication resources are utilized in an efficient manner to provide track lists to all participating vehicles in a mixed-traffic environment. The approach is implemented on SimVille, our indoor testbed for urban driving, in accordance with our system development philosophy. The performance of the approach is evaluated using entropy values of vehicle tracks-an information theoretic measure of uncertainty. The experimental results of our scaled-down tests demonstrate the effectiveness of our approach.