Machine Learning for Safer Autonomous Vehicles: Tackling Traffic Detection and Collision in Smart Cities

Bhupinder Singh, Christian Kaunert, Saurabh Chandra

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Machine learning is enhancing the transportation system and the robotic process automation and artificial intelligence are used in autos to provide intelligent automation (IA), which allows autonomous vehicles to undergo digital transformation. Smart automation has the power to completely replace human interaction, guaranteeing enhanced safety and intelligent vehicle movement. With a comparative study, this article analyzes current approaches that use IoT, AI, and machine learning in autonomous cars. It is critical to comprehend risk-reduction technology as the sector transitions from manual to automated processes. The study outlines the theoretical drawbacks and advantages of autonomous technology, highlighting artificial intelligence's crucial role in vehicle management going forward. It makes recommendations for future lines of inquiry to further the advancement of autonomous car technology. This chapter focuses on the requirements for safety and the difficulties associated with autonomous cars, specifically with regard to object detection, cybersecurity and V2X privacy.
Original languageEnglish
Title of host publicationAchieving Sustainability in Multi-Industry Settings With AI
EditorsMuhammad Syafrudin, Norma Latif Fitriyani, Muhammad Anshari
PublisherIGI Global
Pages237-258
Number of pages22
ISBN (Electronic)9798337325323
ISBN (Print)9798337325309 , 9798337325316
DOIs
Publication statusPublished - 2025

Publication series

NameAdvances in Computational Intelligence and Robotics
PublisherIGI Global
ISSN (Print)2327-0411

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