Driver-automation mutual adaptation: modeling, design, and evaluation of haptic interface for cooperative driving tasks

点击翻译按钮获取中文摘要

Our research focuses on driver-automation mutual adaptation for haptic shared control.In FY 2022, our research achievements are summarized as follows:1. A driver model was developed using a deep learning network and was trained on data collected from a driving simulator experiment with nine participants, and is demonstrated to be effective in predicting individual driver's target trajectory. The driver model is applied for further design of shared control.2. An adaptive control allocation algorithm was developed for lane keeping under an indirect shared control framework. The algorithm uses a trust-based data-driven shared control strategy and blends control inputs of drivers and automation. The automated control agent is computed using Koopman model predictive control. Adaptive control authority allocation is achieved using a hybrid human-to-machine trust model based on a trust mechanism inferred from human-automation interaction and driver input intention. The proposed algorithm is demonstrated to be effective and beneficial in an interactive simulation environment with five participants.3. A steering assistance system involving a shared control strategy was developed for driver override in automated vehicles. The system considers the potential driver demand for override when the vehicle initiates a fail-safe maneuver. A shared control strategy based on driver controllability is adopted to smoothly transfer driving authority when the vehicle is out of danger. The effectiveness of the proposed system is demonstrated in multi-lane highway scenarios.