Reinforcement Learning-Based Autonomous Collision Prediction and Avoidance System for Vehicles

Abstract

This study investigates the feasibility developing an Autonomous Collision Prediction and Avoidance System (RL-ACPAS) based on Reinforcement Learning automobiles in light of the increasing prevalence of autonomous vehicles on the road. In this research, we used a thorough approach that included data gathering from many sensors, RL agent training using deep reinforcement learning algorithms, and performance assessment. The findings show that RL-ACPAS has high rates of avoiding collisions, fast reaction times, and effective decision-making, all of which bode well for enhancing road safety, traffic efficiency, and economic sustainability. These results, combined with hypothetical data and practical consequences, illustrate the transformational potential of RL-ACPAS. The real-world effects on security, effectiveness, economy, and trust are emphasized throughout the presentation. Insights into technological developments, road safety awareness, regulatory issues, and consumer advantages are also provided to readers. Future directions include things like improved learning algorithms, sensor integration, field testing, ethics, human-AI interaction, regulatory frameworks, data sharing, and constant progress. The future of RL-ACPAS is one of experimentation, cooperation, and dedication to making autonomous transportation systems more secure and dependable.