Vehicle detection in driving simulation using extreme learning machine

Automatically driving based on computer vision has attracted more and more attentions from both research and industrial fields. It has two main challenges, high road and vehicle detection accuracy and real-time performance. To study the two problems, we developed a driving simulation platform in a virtual scene. In this paper, as the first step of final solution, the Extreme Learning Machine (ELM) has been used to detect the virtual roads and vehicles. The Support Vector Machine (SVM) and Back Propagation (BP) network have been used as benchmark. Our experimental results show that the ELM has the fastest performance on road segmentation and vehicle detection with the similar accuracy compared with other techniques.