  As an important component of autonomous systems, autonomous car perception
has had a big leap with recent advances in parallel computing architectures.
With the use of tiny but full-feature embedded supercomputers, computer stereo
vision has been prevalently applied in autonomous cars for depth perception.
The two key aspects of computer stereo vision are speed and accuracy. They are
both desirable but conflicting properties, as the algorithms with better
disparity accuracy usually have higher computational complexity. Therefore, the
main aim of developing a computer stereo vision algorithm for resource-limited
hardware is to improve the trade-off between speed and accuracy. In this
chapter, we introduce both the hardware and software aspects of computer stereo
vision for autonomous car systems. Then, we discuss four autonomous car
perception tasks, including 1) visual feature detection, description and
matching, 2) 3D information acquisition, 3) object detection/recognition and 4)
semantic image segmentation. The principles of computer stereo vision and
parallel computing on multi-threading CPU and GPU architectures are then
detailed.
