Machine Learning for Real-time Autonomous Vehicle Traffic Analysis
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[1]
Dr. Miguel Baptista, “Machine Learning for Real-time Autonomous Vehicle Traffic Analysis”, Journal of Bioinformatics and Artificial Intelligence, vol. 3, no. 1, pp. 139–156, Jun. 2024, Accessed: Jul. 17, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/50

Abstract

The design of autonomous vehicles and on-ranch demand gets a technological completion that guarantees the safety of the vehicles and makes each of them travel the shortest time towards their destination. It is essential to analyze its interaction with the rest of the vehicles, which are directly affected by their routes, the trajectory they follow, their behaviors, or the variations thereof. This forms an inherent part of the design of this form of mobility since, in essence, they become autonomous agents. It is fundamental to analyze this interaction at an infra-vehicle level (the vehicle-vehicle interaction when establishing the lane change maneuvers, the interactions in crossings of high traffic) and the interaction at its institutional level (agreements with loading zones, with zones of reduced speed, or with street transformations), with the aim of extracting relevant conclusions on their influence over congestion, travel times, and security.

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