Development of a Vision-based Dilemma Zone Warning System

Dilemma Zone (DZ) is a hazardous area near signalized intersections. This work develops an on-board vision-based system to detect the potential DZ for the warning to drivers. The proposed monocular vision system analyzes parameters of the vehicle and the signalized intersection by detecting and measuring traffic lights, vehicular speed, intersection width, and distance to intersection.
The system contains two modules, which are the processing and recognition module and the DZ judgement module. In the processing and recognition module, a two-stage method is adopted. We perform traffic light detection in stage one through color segmentation, shape filtering and classification. In stage two, we propose a method based on adaptive threshold to obtain the radius of detected traffic lights. Distance and velocity are derived by a monocular vision model. An intersection judgement algorithm is presented for intersections with multiple traffic lights. In the DZ judgement module, DZ calculation is based on a vehicle-oriented DZ definition.
The experimental results show that the proposed system achieves an average precision rate of of 94.56% traffic light detection. The error in distance estimations is from 0.96 meters to 4.83 meters due to he fluctuant characteristics in detected radius. The average Mean Absolute Deviation (MAD) of distance estimation is 2.68 meters. The DZ warning performance is tested with 2 intersections. The accuracy of warning is over 80%. In our discussion, we find the vehicle velocity may have influence on our result and our system has potential to integrate with other vision systems.