Threat prediction algorithm based on local path candidates and surrounding vehicle trajectory predictions for automated driving vehicles

Among others, a reliable threat prediction algorithm is one of the key enabling technologies for the commercialization of the automated driving systems and other driver assistance systems. Previous algorithms that use Time-to-Collision (TTC) as a measure of threat tend to assume constant state and constant input; e.g. constant yaw rate and constant acceleration. Although the predictability of these algorithms is acceptable within a one second time horizon, it becomes invalid for predictions over one second because yaw rate and acceleration are highly unlikely to be constant. Therefore, in this paper, we propose a threat prediction algorithm that can accurately predict TTC over a longer time horizon based on future trajectory predictions of a surrounding vehicle. First, a comprehensive set of local path candidates is generated along the curvilinear coordinates using a quintic (5^th order) polynomial with respect to the arc-length corresponding to the different lateral offsets. Trajectory prediction of a surrounding vehicle is accomplished by introducing target lane detection, which is estimated according to the amount of difference between the current motion and the centerline of the driving lane. Based on these future vehicle trajectories, TTC is computed by comparing the entrance and exit time of two vehicles into and out of the conflict area where the occupied spaces of two vehicles overlap. Finally, in order to provide threat assessment results, the inverse TTC values obtained above are plotted on a 2-dimensional trajectory plane where each set of the tangential acceleration and the initial yaw acceleration values represents each local path candidate. Thus, these threat assessment results can be directly utilized to determine a driving strategy of autonomous vehicles.