Table 1 Literature comparison. Summarises some important research features about IR/IoT-based intelligent transportation systems or smart vehicles, including their description, benefits, numeric results, and challenges.

Abdullah Lakhan et al.¹⁷

IoT workload offloading efficient, intelligent transport systems in federated ACNN integrated cooperated edge-cloud networks

Proposes an augmented convolutional neural network (ACNN) for workload offloading in ITS, incorporating a federated learning scheduling scheme (AFLSS)

AFLSS outperforms existing methods in accuracy and total time; specific numerical results are not provided

High computational requirements for real-time decision-making

Anakhi Hazarika et al.¹⁸

Edge ML Technique for Smart Traffic Management in Intelligent Transportation Systems

Introduces a vision-based Dynamic Traffic Light System (DTLS) using the YOLO algorithm to adjust traffic signals based on real-time traffic density dynamically

After 750 iterations, there was a 94% success rate in task calculations: lane offset accuracy 84.66%, vehicle class accuracy 92.26%, and vulnerable participant accuracy 94.69%

Ensure low computational overhead and maintain real-time performance

Haoxuan Jin and Hongkuan Zhang¹⁹

Smart Vehicle Driving Behaviour Analysis Based on 5G, IoT and Edge Computing Technologies

Proposes a vehicle-splittable task offloading algorithm integrating 5G and edge computing for real-time intelligent vehicle behaviour analysis and path prediction

Achieved a 94% success rate after 750 iterations; high accuracy in lane offset (84.66%), vehicle class judgment (92.26%), and vulnerable participant detection (94.69%)

Managing the complexity of real-time data processing and maintaining high accuracy

Christy Mary Jacob et al.²⁰

An IoT-based Smart Monitoring System for Vehicles

Develops a monitoring system for vehicles to detect traffic violations like speeding, drunken driving, and seat belt usage, sending data to the cloud for analysis

It provides real-time monitoring and automated fine imposition and enhances law enforcement capabilities

Ensuring the reliability and accuracy of sensors and maintaining continuous data connectivity

M. D. Anto Praveena et al.²¹

Smart Car based on IoT

Describes a dual-mode smart car controlled via speech recognition and capable of autonomous movement, with ultrasonic sensors for obstacle detection

Efficient speech-based control; autonomous mode with obstacle detection using ultrasonic sensors

Ensuring robust speech recognition in varying environments and reliable obstacle detection