  Autonomous vehicle refers to a vehicle capable of perceiving its surrounding
environment and driving with little or no human driver input. The perception
system is a fundamental component which enables the autonomous vehicle to
collect data and extract relevant information from the environment to drive
safely. Benefit from the recent advances in computer vision, the perception
task can be achieved by using sensors, such as camera, LiDAR, radar, and
ultrasonic sensor. This paper reviews publications on computer vision and
autonomous driving that are published during the last ten years. In particular,
we first investigate the development of autonomous driving systems and
summarize these systems that are developed by the major automotive
manufacturers from different countries. Second, we investigate the sensors and
benchmark data sets that are commonly utilized for autonomous driving. Then, a
comprehensive overview of computer vision applications for autonomous driving
such as depth estimation, object detection, lane detection, and traffic sign
recognition are discussed. Additionally, we review public opinions and concerns
on autonomous vehicles. Based on the discussion, we analyze the current
technological challenges that autonomous vehicles meet with. Finally, we
present our insights and point out some promising directions for future
research. This paper will help the reader to understand autonomous vehicles
from the perspectives of academia and industry.
