TY - GEN
T1 - Enhancing Driver-Automation Interaction Using RL-Based Shared Control
AU - Koritala, Naveen
AU - Defoort, Michael
AU - Tsai, Chun Wei
AU - Veluvolu, Kalyana C.
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - In autonomous driving, shared control requires optimal adjustment of the relative weight between human driver input and automation control to ensure safety and vehicle stability. This study proposes a Twin Delayed Deep Deterministic Policy Gradient (TD3) Reinforcement Learning (RL) based authority allocation approach incorporating driver behaviour to enhance adaptability and driver-automation collaboration. Simulations conducted in the MATLAB/SIMULINK-CarSim environment demonstrate that the proposed shared control framework significantly reduces lateral offset, heading error, and abrupt steering movements, leading to smoother control transitions and enhanced driving comfort. The results demonstrate the efficacy of the proposed method in enhancing the driver-automation interaction, ensuring a stable, intuitive, and safe shared driving experience.
AB - In autonomous driving, shared control requires optimal adjustment of the relative weight between human driver input and automation control to ensure safety and vehicle stability. This study proposes a Twin Delayed Deep Deterministic Policy Gradient (TD3) Reinforcement Learning (RL) based authority allocation approach incorporating driver behaviour to enhance adaptability and driver-automation collaboration. Simulations conducted in the MATLAB/SIMULINK-CarSim environment demonstrate that the proposed shared control framework significantly reduces lateral offset, heading error, and abrupt steering movements, leading to smoother control transitions and enhanced driving comfort. The results demonstrate the efficacy of the proposed method in enhancing the driver-automation interaction, ensuring a stable, intuitive, and safe shared driving experience.
UR - https://www.scopus.com/pages/publications/105016253047
U2 - 10.1109/ICCA65672.2025.11129816
DO - 10.1109/ICCA65672.2025.11129816
M3 - Conference contribution
AN - SCOPUS:105016253047
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 724
EP - 729
BT - 2025 IEEE 19th International Conference on Control and Automation, ICCA 2025
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Control and Automation, ICCA 2025
Y2 - 30 June 2025 through 3 July 2025
ER -