A Probabilistic Framework for Tracking the Formation and Evolution of Multi-Vehicle Groups in Public Traffic in the Presence of Observation Uncertainties

Future self-driving cars and current ones with advanced driver assistance systems are expected to interact with other traffic participants, which often are multiple other vehicles. Object vehicle tracking forms a key part of resolving this interaction. Furthermore, descriptions of the vehicle group behaviors, like group formations or splits, can enhance the utility of the tracking information for further motion planning and control decisions. In this paper, we propose a probabilistic method to estimate the formation and evolution, including splitting, re-grouping, and so on, of object vehicle groups and the membership conditions for individual object vehicles forming the groups. A Bayesian estimation approach is used to first estimate the states of the individual vehicles in the presence of uncertainties due to sensor imperfections and other disturbances acting on the individual object vehicles. The closeness of the individual vehicles in both their positions and velocity is then evaluated by a probabilistic collision condition. Based on this, a density-based clustering approach is applied to identify the vehicle groups as well as the identity of the individual vehicles in each group. An estimation of the state of the group as well as of the group boundary is also given. Finally, detailed numerical experiments are included, including one on real-time traffic intersection data, to illustrate the workings and the performance of the proposed approach. The potential application of the approach in motion planning of autonomous vehicles is also highlighted.