Tracking moving vehicles using an advanced grid-based Bayesian filter approach

Neuroscientific research suggests that the human brain encodes spatial information in a Bayesian-optimal way by means of distributed, neural population codes. In this paper we apply this concept to Advanced Driver Assistance Systems, introducing a grid-based population code for tracking and predicting the behavior of individual vehicles. The representation encodes a spatially distributed hidden Markov model of current and future vehicle locations and velocities. Predictive information and additional sensory information are integrated over time by means of Bayesian filters. Performance of the system is compared with a Kalman Filter in an overtaking maneuver in a simulated environment. It is shown that the grid-based approach excels Kalman-Filtering performance in several situations, where the Gaussian distribution and linear system assumptions of the Kalman filter are strongly violated. Moreover, the grid-based approach allows the flexible incorporation of additional behavioral assumptions. When the approach assumes that the tracked vehicle will stay in its lane, the probability distribution can be even more favorably focused and unexpected lane changes can be detected.