Bayesian Road Estimation Using Onboard Sensors
Journal article, 2014

This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors, and an inertial measurement unit. We propose a novel road model that is able to describe the road ahead with higher accuracy than the usual polynomial model. We also develop a Bayesian fusion system that uses the following information from the surroundings: lane marking measurements obtained by the camera and leading vehicle and stationary object measurements obtained by a radar–camera fusion system. The performance of our fusion algorithm is evaluated in several drive tests. As expected, the more information we use, the better the performance is.

information fusion

Camera

radar

road geometry

unscented Kalman filter (UKF)

Author

Angel Garcia

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lars Hammarstrand

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Maryam Fatemi

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN)

Vol. PP 99 1-14 6750750

Areas of Advance

Transport

Subject Categories

Probability Theory and Statistics

Signal Processing

DOI

10.1109/TITS.2014.2303811

More information

Created

10/7/2017