AI machine vision for safety and mobility: an autonomous vehicle perspective

Sagar Dasgupta, Xishi Zhu, Muhammad Sami Irfan, Mizanur Rahman, Jiaqi Gong and Steven Jones

Chapter 13 in Handbook on Artificial Intelligence and Transport, 2023, pp 380-409 from Edward Elgar Publishing

Abstract: Machine vision plays a crucial role in the development process of automated mobility, such as enabling the perception abilities of autonomous vehicles (AVs). The precise determination of an AV's perception of its surroundings is necessary for navigating in a dynamic traffic environment, especially in a mixed traffic environment where an AV moves with human-driven vehicles and vulnerable road users such as pedestrians and cyclists. Machine vision enables an AV to detect and classify components of the surrounding environment and perceive itself inside that environment. This information further assists the AV controller in taking appropriate action, including recognizing potential hazards. Machine vision coupled with artificial intelligence (AI) enables an AV to perform natural intelligence tasks and is the basis for replacing a human driver. In addition, AI machine vision-assisted AV eliminates human drivers' potential errors by ensuring better perception of the surroundings. It also assists in quicker and unbiased decision making and the decision-implementation process, thus improving user safety as well as overall mobility. This chapter reviews the basics of machine vision components along with related AI algorithms that will improve road traffic safety and mobility while considering connected AVs and roadside infrastructure. Associated AI machine vision challenges that need to be addressed for robust intelligence are also presented. In addition, future research opportunities related to AI machine vision of automated mobility are discussed.

Keywords: Economics and Finance; Environment; Geography; Innovations and Technology; Law - Academic; Politics and Public Policy Urban and Regional Studies (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.elgaronline.com/doi/10.4337/9781803929545.00023 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 503 Service Temporarily Unavailable

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:elg:eechap:21868_13

Ordering information: This item can be ordered from
http://www.e-elgar.com

Access Statistics for this chapter

More chapters in Chapters from Edward Elgar Publishing
Bibliographic data for series maintained by Darrel McCalla ().