Adaptive AI Architectures for Autonomous Systems: A Hybrid Deep Learning Framework

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ABSTRACT

Vehicles have become an intrinsic part of our lives as one of the most popular ways of private transportation. Even though it provides comfort and safety, private transportation poses road safety risks. Road fatalities are increasing due to traffic, high speed, and driver error. As a result, safety is a top priority in vehicle manufacturing and operation. The advancements in the automobile industry strive to provide increased safety benefits compared to its previous generations. Many modern vehicles include driver assistance systems that aid drivers in various ways. These systems offer helpful information about traffic, congestion levels, blockage, alternative routes to avoid congestion, etc. When a threat is detected, the driver assistance systems may take control of the vehicle from the driver and undertake simple tasks to complex manoeuvres. It also enables road safety, better driving, and reduce fatalities by limiting human error. Such vehicles incorporating the automated driving systems to communicate with the outside world are called Connected and Autonomous Vehicles (CAVs). CAV has emerged as a transformative technology in the automobile sector that has a great potential to change our daily life. Although the ever-increasing use of CAV has numerous advantages, the potential drawbacks, such as security and vulnerability to hacking, are not negligible. CAVs use a variety of sensors to build a virtual map of their surroundings to drive in the correct lane within the speed limit, avoid collisions, and detect obstacles in their immediate physical environment.

Significance of the Study:

This study is significant as it addresses the urgent need for safer and smarter transportation systems by focusing on Connected and Autonomous Vehicles (CAVs). By integrating adaptive AI architectures and hybrid deep learning frameworks, it aims to reduce road fatalities, enhance traffic management, and minimize human error. Furthermore, the study contributes to tackling cybersecurity vulnerabilities, ensuring system reliability, and paving the way for the sustainable adoption of CAVs, thereby transforming global transportation systems.

Summary of the Study:

The study investigates Connected and Autonomous Vehicles (CAVs), emphasizing their potential to enhance road safety and driving efficiency through advanced AI and deep learning technologies. It explores how CAVs use sensors and real-time decision-making to reduce collisions and improve traffic flow. While highlighting the benefits, the study also addresses challenges like cybersecurity threats and infrastructure readiness. By proposing adaptive AI-driven solutions, it provides a roadmap for integrating reliable and secure autonomous systems into modern transportation.