mlsanityData to 7 Oct 2026

Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning

arXiv 2312.16620 ↗First posted 27 Dec 2023eess.SY cs.SYcited by 2

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 11 of its 29 references that are in the map. First posted on arXiv 27 Dec 2023.

Builds on

11 of 29 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  5. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  6. 2018Learning to Walk via Deep Reinforcement Learningcited by 153
  7. 2022Hierarchical Model-Based Imitation Learning for Planning in Autonomous Drivingcited by 43
  8. 2020Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learningcited by 17
  9. 2022Multi-modal Fusion Technology based on Vehicle Information: A Surveycited by 13
  10. 2023AVOID: Autonomous Vehicle Operation Incident Dataset Across the Globecited by 5
  11. 2022MPC Builder for Autonomous Drive: Automatic Generation of MPCs for Motion Planning and Controlcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulatorcited by 7
  2. 2026Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  2. 2022Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning
  3. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving
  4. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning
  5. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey
  6. 2021A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
  7. 2023Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving
  8. 2024An Examination of Offline-Trained Encoders in Vision-Based Deep Reinforcement Learning for Autonomous Driving