4.6 Review

State-of-the-Art Review on Recent Advancements on Lateral Control of Autonomous Vehicles

Journal

IEEE ACCESS
Volume 10, Issue -, Pages 114759-114786

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3217213

Keywords

Blockchains; Automation; Sensors; Collision avoidance; Decision making; Behavioral sciences; Autonomous automobiles; AV; CPS; CAV; DCPS; IoV; DSRC; V2X; CAV-UAV; decision-making; blockchain; on-board units

Funding

  1. Qatar National Library

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The most well-known research in the field of driverless vehicles is currently focused on connected autonomous vehicles (CAVs), which not only enhance logistics operations but also offer new possibilities for sustainable growth in the industry. This review examines the cyber physical aspect of autonomous vehicles, incorporating cloud-controlled wireless network-based models and unmanned aerial vehicles (UAVs). The study also explores Vehicle-to-Everything (V2X) communication and its impact on lateral control, particularly lane changes, considering social behavior and human driver tendencies.
The most well-known research on driverless vehicles at the moment is connected autonomous vehicles (CAVs), which reflects the future path for the self driving field. The development of connected autonomous vehicles (CAVs) is not only increasing logistics operations, but it is also opening up new possibilities for the industry's sustainable growth. In this review, we will explore the cloud-controlled wireless network-based model of cyber physical aspect of the autonomous vehicle, which is coupled with unmanned aerial vehicles (UAVs). Additionally, this model is Internet of Things (IoT) managed and AI-based, with a blockchain-based security mechanism. Additionally, we'll focus on lateral control in autonomous driving, particularly the lane change maneuver, taking social behavior into account. Here, we briefly reviewed Vehicle-to-Everything (V2X) communication, which is carried out by on-board sensors and connected wireless medium that enhance the lane departure processes while retaining human driver behavior relying on obstacle avoidance.

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