Hypothesis based vehicle detection for increased simplicity in multi-sensor ACC

Abstract

Systems for adaptive cruise control (ACC) become increasingly complex in case multiple sensors are used. The search space, detection error and run-time may increase substantially due to combinatory explosion of methods and data. This paper presents a method that simplifies fusion between range and vision devices using corresponding sets of hypotheses. A system is proposed that combines three modules: one uses output of a 24 GHz radar device, one uses single images from a monocular camera system; and one uses the image sequence data of the same system. The radar detection module uses condensation tracking. The vehicle detection module uses scaled symmetry detection. The three modules are fused by sharing sets of hypotheses for detection of vehicles. Results show 96% error reduction with respect to range sensing only and 63% detection increase due to tracking.