  Deep learning-based intelligent vehicle perception has been developing
prominently in recent years to provide a reliable source for motion planning
and decision making in autonomous driving. A large number of powerful deep
learning-based methods can achieve excellent performance in solving various
perception problems of autonomous driving. However, these deep learning methods
still have several limitations, for example, the assumption that lab-training
(source domain) and real-testing (target domain) data follow the same feature
distribution may not be practical in the real world. There is often a dramatic
domain gap between them in many real-world cases. As a solution to this
challenge, deep transfer learning can handle situations excellently by
transferring the knowledge from one domain to another. Deep transfer learning
aims to improve task performance in a new domain by leveraging the knowledge of
similar tasks learned in another domain before. Nevertheless, there are
currently no survey papers on the topic of deep transfer learning for
intelligent vehicle perception. To the best of our knowledge, this paper
represents the first comprehensive survey on the topic of the deep transfer
learning for intelligent vehicle perception. This paper discusses the domain
gaps related to the differences of sensor, data, and model for the intelligent
vehicle perception. The recent applications, challenges, future researches in
intelligent vehicle perception are also explored.
