Single Filter Lead Vehicle Distance and Velocity Estimation with Multiple Hypothesis Testing

Abstract

This paper presents a monocular camera-based lead vehicle distance and velocity estimation algorithm for automotive application. With an initial guess of real width of the lead vehicle, a Kalman Filter gives estimates for relative distance, velocity and acceleration. The still unknown scale factor to the real size is then statistically estimated from multiple hypothesis using vertical triangulation measurements. Camera pitching motion effects are compensated through the estimation of the vanishing point. The real relative distance, velocity and acceleration can be obtained with the estimated scale factor. The developed method is evaluated in simulations considering the Euro NCAP forward collision warning and emergency braking test procedures, the braking dynamics of vehicles, multiple lead vehicle sizes, periodic camera pitching disturbance, pixelization and vanishing point estimation errors and a wide range of velocities from 10km/h to 130km/h. The results are promising and so real life evaluation is the goal of future development.