AGENTIC AI FOR AUTONOMUS VEHICLES

Abstract

The Agentic AI for Autonomous Vehicles project focuses on the design and development of an intelligent AI-driven system capable of autonomous navigation, real-time decision-making, and adaptive driving behavior. Traditional autonomous vehicle systems mainly depend on predefined rule-based algorithms, which often face difficulties in handling unpredictable road situations, dynamic environments, and complex human behavior. To overcome these limitations, the proposed system introduces an Agentic AI Architecture where the autonomous vehicle functions as an intelligent agent capable of observing, reasoning, learning, and acting independently in real time. The system integrates advanced technologies such as Artificial Intelligence (AI), Machine Learning, Reinforcement Learning, Computer Vision, and Multi-Modal Perception to improve environmental understanding and driving safety. The MultiModal Perception layer collects and processes data from sensors such as cameras, LiDAR, radar, GPS, and ultrasonic sensors to identify roads, traffic signs, pedestrians, vehicles, and obstacles. The processed information is then analyzed by a Reasoning Engine that uses Large Language Models (LLMs) and Reinforcement Learning techniques to predict traffic behavior, interpret complex scenarios, and make intelligent driving decisions. Unlike traditional sensing-acting systems, the proposed architecture follows an observing-reasoning-acting cycle, enabling the vehicle to perform human-like decision-making in uncertain and rapidly changing environments. The system can dynamically adapt to traffic conditions, predict pedestrian intent, optimize route planning, and execute safe driving maneuvers such as braking, lane changing, overtaking, and collision avoidance. The project demonstrates how Agentic AI can significantly enhance the reliability, adaptability, and safety of autonomous vehicles while moving closer to fully intelligent transportation systems.