The inflexible human-autonomy relationship within autonomous driving scenarios still has not realized synergetic intelligence, therefore unable to provide adaptive and context-sensitive decision-making and sometimes leading to violation of human pReferences or even hazards. In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. The proposed algorithm uses the result of driving risk reasoning as one input of reinforcement learning combining fNIRS-based risk and driving safety field model-based risk, realizing integrating human brain activity into the reinforcement learning scheme, then overcoming the disadvantage of machine-oriented intelligence that could violate human intentions. To achieve policy learning within limited BiTL training periods, we add two modification features to the proposed algorithm based on TD3. The experiment involving twenty participants has been conducted, and the results show that in continuously high-risk driving scenarios, compared to traditional reinforcement learning algorithms without human participation, the proposed algorithm can maintain a cautious driving policy and avoid potential collisions, validated with both proximal surrogate indicators and success rates.

The utilization of shared control in the realm of surgical robotics augments precision and safety by amalgamating human expertise with autonomous assistance. This paper proposes a user-centered shared control framework enabling a robot to learn from expert demonstration, predict operators’ intent and modulate control authority to provide natural assistance when needed. We employ deep inverse reinforcement learning (IRL) to enable the robot to learn path planning from expert demonstrations with fast convergence, subsequently enhancing the policy with a potential field method. The control authority is allocated seamlessly between the human operator and the autonomous agent based on the prediction of operators’ movement from an adaptive filter and fuzzy logic inference. The proposed method is executed using the da Vinci Research Kit (dVRK) robot in a simulation environment, and its effectiveness is assessed through user performance evaluation in a trajectory tracking task. Compared to direct control and simple shared control, the proposed shared control scheme exhibits superior tracking accuracy and trajectory smoothness under external disturbances. Subjective responses underscore users’ perception of the method’s efficacy in enhancing their performance.