Estimation of collective maneuvers through cooperative multi-agent planning

Abstract

In order to determine a cooperative driving strategy, it is beneficial for an autonomous vehicle to incorporate the intended motion of surrounding vehicles within its own motion planning. However, as intentions cannot be measured directly and the motion of multiple vehicles often are highly interdependent, this incorporation has proven challenging. In this paper, the problem of maneuver estimation is addressed, focusing on situations with close interaction between traffic participants. Therefore, we define collective maneuvers based on trajectory homotopy, describing the relative motion of multiple vehicles in a scene. Representing maneuvers by sample trajectories, maneuver-dependent prediction models of the vehicle states can be defined. This allows for a Bayesian estimation of maneuver probabilities given observations of the real motion. The approach is evaluated by simulation in overtaking scenarios with oncoming traffic and merging scenarios at an intersection.