In the concept of autonomous cars, pedestrian safety is one of the important factors. So there is a need for the improvement of the safety of pedestrians with growing number of automobiles and automobile technologies. There have been many research and development works going on in the area of pedestrian detection and distance estimation. And Data fusion has become one of the most talked about topics in the area of Advanced Driver Assistance System (ADAS) in the recent years. This paper attempts to use a data fusion technique to detect pedestrian as well as to estimate the distance of the pedestrian using Light Emitting Diode Detection and Ranging (Leddar) and Images sensor. The main problem in this data fusion is combining the output of the Leddar sensor with that of the image sensor. A unique method of correlating the Leddar output with image sensor output is identified and an algorithm is developed using this method which can identify Region of Interests (ROIs) in the image sensor output with respect to the Leddar sensor output. The ROIs will then be processed using machine learning algorithms to detect pedestrians. It is found that this data fusion is able to identify whether there is a pedestrian along with the distance of the pedestrian.
V. D, Dr. K. I. Ramachandran, and S, A., “Pedestrian Detection using Data Fusion of Leddar Sensor and Visual Camera with the help of Machine Learning”, International Journal of Pure and Applied Mathematics, vol. 118, 20 vol., 2018.