Navigating Tomorrow: Advancements and Road Ahead in AI for Autonomous Vehicles

EasyChair Preprint 11955

Abstract

The field of autonomous vehicles has witnessed remarkable strides propelled by cutting-edge Artificial Intelligence (AI) technologies. This abstract delves into the state-of-the-art developments, presenting a comprehensive overview of the current landscape and envisioning the future trajectories in AI for autonomous vehicles. The advancements in machine learning, computer vision, and sensor technologies have synergistically converged to empower self-driving vehicles with enhanced perception, decision-making capabilities, and overall safety. The integration of deep learning algorithms has significantly improved object recognition and scene understanding, enabling vehicles to navigate complex and dynamic environments with unprecedented accuracy. Real-time data processing and sensor fusion techniques play a pivotal role in creating a holistic perception system that interprets information from various sensors, such as LiDAR, radar, and cameras. Furthermore, AI-based predictive modeling enhances the vehicle's ability to anticipate and respond to potential hazards, ensuring a proactive approach to safety. Looking ahead, the abstract explores future directions in AI for autonomous vehicles, highlighting the ongoing research and development efforts aimed at overcoming existing challenges. This includes addressing edge cases, refining decision-making processes in unpredictable scenarios, and fostering robust communication systems among autonomous vehicles.

Keyphrases: Artificial Intelligence, autonomous vehicles, computer vision, deep learning, machine learning, sensor Technologies

@booklet{EasyChair:11955,
  author    = {William Jack and Butler Jon},
  title     = {Navigating Tomorrow: Advancements and Road Ahead in AI for Autonomous Vehicles},
  howpublished = {EasyChair Preprint 11955},
  year      = {EasyChair, 2024}}