Artificial intelligence applications in the development of autonomous vehicles: a survey
Abstract
The advancement of artificial intelligence (AI) has truly stimulated the development and deployment of autonomous vehicles (AVs) 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 (i.e., self-driving), 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) perception; 2) localization and mapping; and 3) 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) high definition maps, big data, and high performance computing; 2) augmented reality (AR)/virtual reality (VR) enhanced simulation platform; and 3) 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.
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The neural network of Sales and Correa is utilized to perceive and quickly learn the surroundings and suggests unmanned machines for navigating autonomous vehicles under urban road conditions.
“Te NN of Sales and Correa, which is utilized to perceive and quickly learn the surroundings and suggests unmanned machines for navigating AVs under urban road conditions, is one of the good uses of object detection and pattern recognition learning.”
From Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges (2023) · cites this paper as [95]
Akemi has done work regarding path planning of autonomous vehicles using sliding mode control mode.
“Akemi has done similar work regarding the path planning of AVs using sliding mode control mode [95].”
From Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges (2023) · cites this paper as [95]
SLAM techniques are often employed for tasks like item localization, pedestrian recognition, and locating the UAV that calls for updating a map of an ambiguous region while keeping an eye on a moving object.
“SLAM techniques are often employed for tasks like item localization, pedestrian recognition, and locating the UAV that calls for updating a map of an ambiguous region while keeping an eye on a moving object [95].”
From Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges (2023) · cites this paper as [95]
These technologies enable vehicles to perceive and interpret their surroundings, make decisions based on data, and navigate roads safely without human intervention.
“These technologies enable vehicles to perceive and interpret their surroundings, make decisions based on data, and navigate roads safely without human intervention [81] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [81]
AI, ML, and DL are essential components of autonomous vehicles.
“Autonomous Vehicles: AI, ML, and DL are essential components of autonomous vehicles.”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [81]
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Artificial intelligence, machine learning and deep learning in advanced robotics, a review
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“Akemi has done similar work regarding the path planning of AVs using sliding mode control mode [95].”
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- Authors
- Ma, Y., Wang, Z., Yang, H., & Yang, L.
- Affiliations
- Old Dominion University (US)
- Huazhong Agricultural University (CN)
- Venue
- IEEE/CAA Journal of Automatica Sinica
- Published
- 2020-03
- Volume
- 7
- Issue
- 2
- Pages
- 315-329
- ISSN
- 2329-9266 (ISSN-L)
- 2329-9274
- OpenAlex
- W3012481664
- Cited by
- 639
- References
- 1
- Citations by year
- 2026: 72 · 2025: 131 · 2024: 172 · 2023: 110 · 2022: 86 · 2021: 53 · 2020: 15
- FWCI
- 24.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE/CAA Journal of Automatica Sinica (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- autonomous vehicles, artificial intelligence, autonomous driving, perception, localization and mapping, decision-making, high-definition maps, big data, high-performance computing, augmented reality simulation, virtual reality simulation, 5G, connected vehicles
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