Hybrid human-centric haptic shared control using artificial neural network and model predictive control

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Commercially available Lane Keeping Assist systems fail to consider the driver's intentions since they mainly focus on minimising path tracking errors, resulting in conflicts between humans and automation. This often leads to users being unsatisfactory and turning off the assist, as a result diminishing the advantages such as reduced workload and increased road safety. Considering a driver model in the assist helps increase user acceptance. Therefore, we propose a torque-based hybrid controller for a human-centric haptic shared Lane Keeping Assist, pairing a data-driven driver model with a model-based controller to foster the collaboration between the driver and assist. First, the driver's non-linear steering wheel torque behaviour is modelled and predicted using a Bidirectional Long Short-Term Memory network with an accuracy ≥72.4% and a smoothness ≥0.85Nm/s over a 0.4s prediction horizon. Second, a Model Predictive Controller with a linear bicycle and steering model is developed, where it utilises the driver model's predictions as a time-varying reference. We developed three human-centric controllers for comparison and used a state-of-the-art commercial solution as the baseline controller. The experiments were performed in Toyota Motor Europe's fixed-base driving simulator, where 15 participants tested and evaluated the four controllers. The results show a 113.1% increase in collaborative ratio while maintaining a similar path tracking performance compared to the baseline.