Robotic Technologies and Intelligent Systems in Autonomous Vehicles: A Comprehensive Review

Abstract

Autonomous vehicles (AVs) represent one of the most transformative developments in modern transportation systems. By integrating robotics, artificial intelligence, and advanced sensing technologies, autonomous vehicles are capable of perceiving their environment, making intelligent decisions, and executing driving tasks with minimal or no human intervention. This study presents a comprehensive review of robotic technologies used in autonomous vehicle systems, focusing on the fundamental components that enable perception, planning, decision-making, and control. The study analyses recent advancements in sensor technologies, intelligent algorithms, and vehicle communication systems that collectively contribute to the development of safe and reliable autonomous driving. The research highlights the importance of perception systems that allow autonomous vehicles to understand their surrounding environment. Modern autonomous vehicles utilize multiple sensors such as LiDAR, radar, cameras, ultrasonic sensors, and inertial measurement units (IMU) to collect real-time data about the road environment. Sensor fusion techniques combine data from these sensors to generate accurate environmental models and improve object detection, localization, and obstacle recognition. The integration of these sensing technologies significantly enhances situational awareness and enables vehicles to operate safely even in complex and dynamic traffic conditions. Another major focus of this paper is behaviour-aware motion planning and decision-making algorithms used in autonomous vehicles. Intelligent algorithms such as reinforcement learning, Markov decision processes, deep neural networks, and game theory enable autonomous vehicles to predict the behaviour of surrounding vehicles and pedestrians. These algorithms help vehicles determine optimal driving actions such as lane changing, speed adaptation, and obstacle avoidance while maintaining safety and efficiency. Additionally, vehicle control strategies including proportional-integral-derivative (PID) control and model predictive control (MPC) are discussed as key techniques for ensuring accurate trajectory tracking and vehicle stability.