Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning

2023/12/27 by Amin Jalal Aghdasian, Amirhossein Heydarian Ardakani, Aghdasian, Amin Jalal +5
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Autonomous Vehicle Technology and Safety #FOS: Electrical engineering #Reinforcement Learning in Robotics #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2312.16620

openalex publication_date 2023/12/27 · openalex created_date 2023/12/30 · openalex updated_date 2026/07/28

Abstract

This paper proposes a novel approach by integrating sensor fusion with deep reinforcement learning, specifically the Soft Actor-Critic (SAC) algorithm, to develop an optimal control policy for self-driving cars. Our system employs a two-branch fusion method for vehicle image and tracking sensor data, leveraging the strengths of residual structures and identity mapping to enhance agent training. Through comprehensive comparisons, we demonstrate the efficacy of information fusion and establish the superiority of our selected algorithm over alternative approaches. Our work advances the field of autonomous driving and demonstrates the potential of reinforcement learning in enabling intelligent vehicle decision-making.

Related

• Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving
• Robust AI Driving Strategy for Autonomous Vehicles
• A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning
• End-to-end Autonomous Driving using Deep Learning: A Systematic Review
• Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles