Decision-making and safety enhancement in next-generation autonomous vehicles: A comparative analysis with deep learning

The rapid evolution of Artificial Intelligence (AI) is reshaping autonomous vehicle (AV) systems, enhancing decision-making, navigation, and vehicular safety. However, real-time responsiveness, reliable object detection, adaptive path planning, and resilience to adversarial threats remain significant challenges. This study aims to improve AV safety, adaptability, and performance by integrating advanced AI techniques and benchmarking them against conventional rule-based methods to highlight strengths and limitations. The study utilized a multi-phase analytical and experimental framework that integrated reinforcement learning-based navigation through simulation environments with deep learning perception models. Robust environmental modeling was achieved by integrating LiDAR, radar, and camera data using hybrid sensor fusion techniques. The latency and predictive accuracy were evaluated using real-time computing systems. Long Short-Term Memory (LSTM) networks for trajectory prediction, Deep Neural Networks (DNNs), and Convolutional Neural Networks (CNNs) for object detection, and Reinforcement Learning (RL) for adaptive decision-making in dynamic situations were important AI techniques. While hybrid sensor fusion enhanced perception of the surroundings, neuromorphic computing was investigated for low-latency, energy-efficient processing. The study supports future directions for safe, scalable, and morally sound autonomous mobility systems while confirming AI's ability to handle functional AV challenges.