Artificial intelligence applications in the development of autonomous vehicles: a survey - Ma, Yifang; Wang, Zhenyu; Yang, Hong; Yang, Lin (IEEE/CAA journal of automatica sinica, 2020)
The advancement of artificial intelligence &#x0028 AI &#x0029 has truly stimulated the development and deployment of autonomous vehicles &#x0028 AVs &#x0029 in the transportation industry. Fueled by big data from various sensing devices and advanced computing resources, AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion. To achieve goal of full automation &#x0028 i.e., self-driving &#x0028 , it is important to know how AI works in AV systems. Existing research have made great efforts in investigating different aspects of applying AI in AV development. However, few studies have offered the research community a thorough examination of current practices in implementing AI in AVs. Thus, this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue. Specifically, it intends to analyze their use of AIs in supporting the primary applications in AVs: 1&#x0029 perception; 2&#x0029 localization and mapping; and 3&#x0029 decision making. It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation. Based on the exploration of current practices and technology advances, this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies: 1&#x0029 high definition maps, big data, and high performance computing; 2&#x0029 augmented reality&#x0028 AR &#x0029 &#x002F virtual reality &#x0028 VR &#x0029 enhanced simulation platform; and 3&#x0029 5G communication for connected AVs. This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.