Pedestrian Detection for Autonomous Driving within Cooperative Communication System

Abstract

The ability to perceive and understand surrounding road-users behaviors is crucial for self-driving vehicles to correctly plan reliable reactions. Computer vision that relies mostly on machine learning techniques enables autonomous vehicles to perform several required tasks such as pedestrian detection. Furthermore, within a fully autonomous driving environment, driverless vehicle has to communicate and share perceived data with its neighboring vehicles for more safe navigation. In this context, our paper proposes a warning notification diffusion solution related to real-time pedestrian presence detection, through an inter-vehicle communication system. To achieve this purpose, pedestrian and vehicle recognition is required. Thus, we implemented intended detectors. We used Histogram of Oriented Gradients (HOG) descriptor with the linear Support Vector Machine (SVM) classifier for the pedestrian detector, and Haar feature-based cascade classifier to reach vehicle detection. The performance evaluation of our solution leads to fairly good detection accuracy around 90% for pedestrian and 88% for vehicle.