  For intelligent vehicles, sensing the 3D environment is the first but crucial
step. In this paper, we build a real-time advanced driver assistance system
based on a low-power mobile platform. The system is a real-time multi-scheme
integrated innovation system, which combines stereo matching algorithm with
machine learning based obstacle detection approach and takes advantage of the
distributed computing technology of a mobile platform with GPU and CPUs. First
of all, a multi-scale fast MPV (Multi-Path-Viterbi) stereo matching algorithm
is proposed, which can generate robust and accurate disparity map. Then a
machine learning, which is based on fusion technology of monocular and
binocular, is applied to detect the obstacles. We also advance an automatic
fast calibration mechanism based on Zhang's calibration method. Finally, the
distributed computing and reasonable data flow programming are applied to
ensure the operational efficiency of the system. The experimental results show
that the system can achieve robust and accurate real-time environment
perception for intelligent vehicles, which can be directly used in the
commercial real-time intelligent driving applications.
