Lane prediction optimization in VANET

TL;DR

This paper explores optimizing lane prediction in VANETs to enhance safety, energy efficiency, and driver convenience by leveraging V2V technology for better lane change decisions.

Contribution

It introduces a novel approach for lane prediction optimization in VANETs, improving decision-making accuracy for lane changes.

Findings

Enhanced lane prediction accuracy in VANETs.

Improved safety and energy efficiency metrics.

Better driver convenience through predictive technology.

Abstract

Among the current advanced driver assistance systems, Vehicle-to-Vehicle (V2V) technology has great potential to increase Vehicular Ad Hoc Network (VANET) performance in terms of security, energy efficiency, and comfortable driving. In reality, vehicle drivers regularly change lanes depending on their assumptions regarding visual distances. However, many systems are not quite well-designed, because the visible range is limited, making it difficult to achieve such a task. V2V technology offers high potential for VANET to increase safety, energy efficiency, and driver convenience. Drivers can make more intelligent options in terms of lane selection using predicted information of downstream lane traffic, which is essential for obtaining mobility benefits.