Autonomous Vehicles: Challenges and Solutions in Perception and Decision Making

Abstract

Autonomous vehicles (AVs) have the potential to revolutionize transportation by enhancing safety, efficiency, and mobility. However, their widespread adoption is hindered by significant challenges in perception and decision-making. Perception systems must accurately interpret complex and dynamic environments using sensor data from cameras, LiDAR, RADAR, and other sources. Challenges such as sensor limitations, adverse weather conditions, and occlusions can degrade perception accuracy. Meanwhile, decision-making in AVs involves real-time path planning, obstacle avoidance, and adherence to traffic rules, which require robust algorithms capable of handling uncertainty and unpredictable human behavior. To address these challenges, advanced machine learning techniques, sensor fusion methods, and edge computing solutions have been proposed to improve perception accuracy and response time. Additionally, deep reinforcement learning, probabilistic models, and rule-based decision frameworks contribute to safer and more adaptable autonomous navigation. This paper explores the key challenges in AV perception and decision-making and discusses state-of-the-art solutions that enhance system reliability. Future research should focus on improving generalization across diverse driving conditions, enhancing interpretability, and integrating human-in-the-loop strategies for safer and more efficient autonomous driving.