Incorporating environmental knowledge into Bayesian filtering using attractor functions

Abstract

Many automotive systems use linear approaches to track and predict other traffic participants. While this may be appropriate on highways, linear predictions do not work properly on curved roads or lane crossings. This contribution introduces a generic way for including environmental knowledge - such as the lane trajectory ahead - to anticipate yaw rate and acceleration of other traffic participants. The anticipatory knowledge is used to improve prediction in filtering tasks. It is embedded in a Bayesian framework by introducing attractors, which modify the probabilistic propagation of state estimations. The attractors model how traffic participants typically behave, given environmental knowledge such as lane information, traffic lights, or indicator lights. We demonstrate the potential of this approach by modeling the fact that vehicles usually stay in their lane. We show that given correct context information and nonlinear traffic situations, the tracking error is considerably lower compared to conventional tracking methods. In addition, we also show that the intentions of other traffic participants may be inferred by comparing actual sensory data with anticipated probability distributions, which were generated dependent on alternative attractors.