Tracking and detection of lane and vehicle integrating lane and vehicle information using PDAF tracking model

Abstract

We propose a robust system for multi-vehicle and multi-lane detection with integrating lane and vehicle information. Most research work only can detect the lanes or vehicles separately. However, the dependency between lane information and vehicle information are able to support each other achieving more reliable results. We use probabilistic data association filter to integrate the information of lane and vehicle. In probabilistic data association filter, cumulate history of target is kept in the data association probability. Target tracking can improve the detection results through region of interests. At the same time, a high-level traffic model combines the lane and vehicle information. The tracking and detection can benefit each other through iterations. Experimental results show that our approach can detect multi-vehicle and multi-lane reliably.