A workload adaptive haptic shared control scheme for semi-autonomous driving

Modeling Human Steering Behavior in Haptic Shared Control of Autonomy-Enabled Unmanned Ground Vehicles

OBJECTIVE

A human steering model for teleoperated driving is extended to capture the human steering behavior in haptic shared control of autonomy-enabled Unmanned Ground Vehicles (UGVs).

BACKGROUND

Prior studies presented human steering models for teleoperation of a passenger-sized Unmanned Ground Vehicle, where a human is fully in charge of driving. However, these models are not applicable when a human needs to interact with autonomy in haptic shared control of autonomy-enabled UGVs. How a human operator reacts to the presence of autonomy needs to be studied and mathematically encapsulated in a module to capture the collaboration between human and autonomy.

METHOD

Human subject tests are conducted to collect data in haptic shared control for model development and validation. The ACT-R architecture and two-point steering model used in the previous literature are adopted to predict the operator's desired steering angle. A torque conversion module is developed to convert the steering command from the ACT-R model to human torque input, thus enabling haptic shared control with autonomy. A parameterization strategy is described to find the set of model parameters that optimize the haptic shared control performance in terms of minimum average lane keeping error (ALKE).

RESULTS

The model predicts the minimum ALKE human subjects achieve in shared control.

CONCLUSIONS

The extended model can successfully predict the best haptic shared control performance as measured by ALKE.

APPLICATION

This model can be used in place of human operators, enabling fully simulation-based engineering, in the development and evaluation of haptic shared control technologies for autonomy-enabled UGVs, including control negotiation strategies and autonomy capabilities.

Haptic shared control improves neural efficiency during myoelectric prosthesis use

Clinical myoelectric prostheses lack the sensory feedback and sufficient dexterity required to complete activities of daily living efficiently and accurately. Providing haptic feedback of relevant environmental cues to the user or imbuing the prosthesis with autonomous control authority have been separately shown to improve prosthesis utility. Few studies, however, have investigated the effect of combining these two approaches in a shared control paradigm, and none have evaluated such an approach from the perspective of neural efficiency (the relationship between task performance and mental effort measured directly from the brain). In this work, we analyzed the neural efficiency of 30 non-amputee participants in a grasp-and-lift task of a brittle object. Here, a myoelectric prosthesis featuring vibrotactile feedback of grip force and autonomous control of grasping was compared with a standard myoelectric prosthesis with and without vibrotactile feedback. As a measure of mental effort, we captured the prefrontal cortex activity changes using functional near infrared spectroscopy during the experiment. It was expected that the prosthesis with haptic shared control would improve both task performance and mental effort compared to the standard prosthesis. Results showed that only the haptic shared control system enabled users to achieve high neural efficiency, and that vibrotactile feedback was important for grasping with the appropriate grip force. These results indicate that the haptic shared control system synergistically combines the benefits of haptic feedback and autonomous controllers, and is well-poised to inform such hybrid advancements in myoelectric prosthesis technology.

Adaptive authority allocation-based driver-automation shared control for autonomous vehicles

To avoid crashes caused by driver error in avoiding obstacles, a driver-centered steering assist controller with an adaptive authority allocation system is proposed for cooperative control purposes. To begin with, a concept of space collision risk (SCR) is introduced creatively for assessing the vehicle's safety status in which the relative distance and the relative angle between the vehicle and the obstacle are taken into consideration. Meanwhile, the SCR-based authority allocation system is addressed to allocate steering authorities between the human driver and the assist controller adaptively to reduce SCR to zero as soon as possible. After that, an autonomous steering controller based on the model predictive control (MPC) technique and the artificial potential field (APF) method, considering not only the vehicle stability constraints but also roads and obstacles constraints, is developed to aid the human driver when necessary. In the end, the proposed algorithm is simulated in the CarSim-Simulink co-simulation platform under a series of typical scenarios, which shows the feasibility and effectiveness of the presented shared steering control method.