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For intelligent vehicles, sensing the 3D environment is the first but crucialstep. In this paper, we build a real-time advanced driver assistance systembased on a low-power mobile platform. The system is a real-time multi-schemeintegrated innovation system, which combines stereo matching algorithm withmachine learning based obstacle detection approach and takes advantage of thedistributed computing technology of a mobile platform with GPU and CPUs. Firstof all, a multi-scale fast MPV (Multi-Path-Viterbi) stereo matching algorithmis proposed, which can generate robust and accurate disparity map. Then amachine learning, which is based on fusion technology of monocular andbinocular, is applied to detect the obstacles. We also advance an automaticfast calibration mechanism based on Zhang's calibration method. Finally, thedistributed computing and reasonable data flow programming are applied toensure the operational efficiency of the system. The experimental results showthat the system can achieve robust and accurate real-time environmentperception for intelligent vehicles, which can be directly used in thecommercial real-time intelligent driving applications.

A Robust Real-Time Computing-based Environment Sensing System for Intelligent Vehicle