Semi-autonomous Ground Vehicles Research Articles

Communication and Interaction With Semiautonomous Ground Vehicles by Force Control Steering.

While full automation of road vehicles remains a future goal, shared-control and semiautonomous driving-involving transitions of control between the human and the machine-are more feasible objectives in the near term. These alternative driving modes will benefit from new research toward novel steering control devices, more suitably where machine intelligence only partially controls the vehicle. In this article, it is proposed that when the human shares the control of a vehicle with an autonomous or semiautonomous system, a force control, or nondisplacement steering wheel (i.e., a steering wheel which does not rotate but detects the applied torque by the human driver) can be advantageous under certain schemes: tight rein or loose rein modes according to the H -metaphor. We support this proposition with the first experiments to the best of our knowledge, in which human participants drove in a simulated road scene with a force control steering wheel (FCSW). The experiments exhibited that humans can adapt promptly to force control steering and are able to control the vehicle smoothly. Different transfer functions are tested, which translate the applied torque at the FCSW to the steering angle at the wheels of the vehicle; it is shown that fractional order transfer functions increment steering stability and control accuracy when using a force control device. The transition of control experiments is also performed with both: a conventional and an FCSW. This prototypical steering system can be realized via steer-by-wire controls, which are already incorporated in commercially available vehicles.

A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles

This paper proposes a robust control framework for lane-keeping and obstacle avoidance of semiautonomous ground vehicles. It presents a systematic way of enforcing robustness during the MPC design stage. A robust nonlinear model predictive controller (RNMPC) is used to help the driver navigating the vehicle in order to avoid obstacles and track the road centre line. A force-input nonlinear bicycle vehicle model is developed and used in the RNMPC control design. A robust invariant set is used in the RNMPC design to guarantee that state and input constraints are satisfied in the presence of disturbances and model error. Simulations and experiments on a vehicle show the effectiveness of the proposed framework.