State of the Art and Future Directions of Deep Learning Driven Collision Avoidance Mechanisms in Vehicular Ad Hoc Networks

Abstract

Evolution of Intelligent Transportation Systems (ITS) has led to the transformation of modern vehicular safety strategies, particularly due to incorporation of Artificial Intelligence (AI) in collision avoidance technologies. These AI-enabled systems are designed in order to minimize the risk of accidents. To achieve this, real-time data is exchanged through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication frameworks. This study explores the Technological Components of Autonomous Vehicular Systems, Core components of Intelligent Transportation Systems (ITS), Methodology to be adopted in AI-based collision avoidance Systems and Use case of AI-based collision avoidance System thereby emphasizing the role of connected vehicle communication in enhancing awareness and minimizing response delays. It also discusses the contribution of various AI methodologies, as in predictive modeling and reinforcement learning, which help systems learn from previous events and dynamically adjust to new traffic conditions. Few challenges identified are sensor inaccuracies, latency in data transmission, cybersecurity risks, and privacy concerns which must be addressed for ITS systems to achieve broader adoption. The future scope proposed in this area is incorporating emerging technologies such as 5G connectivity, for enabling ultra-fast communication speeds; edge computing, which facilitates for local and immediate data processing; and federated learning, which enables AI model training across distributed devices without the need for centralized data storage.

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