The intelligent copilot: A constraint-based approach to shared-adaptive control of ground vehicles

Abstract

This paper presents a new approach to semi-autonomous vehicle hazard avoidance and stability control, based on the design and selective enforcement of constraints. This differs from traditional approaches that rely on the planning and tracking of paths and facilitates "minimally-invasive" control for human-machine systems. Instead of forcing a human operator to follow an automation-determined path, the constraint-based approach identifies safe homotopies, and allows the operator to navigate freely within them, introducing control action only as necessary to ensure that the vehicle does not violate safety constraints. This method evaluates candidate homotopies based on "restrictiveness," rather than traditional measures of path goodness, and designs and enforces requisite constraints on the human's control commands to ensure that the vehicle never leaves the controllable subset of a desired homotopy. This paper demonstrates the approach in simulation and characterizes its effect on human teleoperation of unmanned ground vehicles via a 20-user, 600-trial study on an outdoor obstacle course. Aggregated across all drivers and experiments, the constraintbased control system required an average of 43% of the available control authority to reduce collision frequency by 78% relative to traditional teleoperation, increase average speed by 26%, and moderate operator steering commands by 34%.

Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model

“…The methods developed by [25] and [22] construct corridors consisting of multiple convex regions to describe the area that the vehicle will drive through. Based on the corridors and constant velocity, they apply constraints on the lateral position of the vehicle to avoid colliding with obstacles yielding a convex optimization problem with global optimality.…”

“…A deviation of the system will only become noticeable if safety constraints are active and the system will be perceived as inactive to the human driver otherwise. A study on driving in shared safety systems [25] underlines the importance: Human drivers approved of a surprisingly high amount of intervention if their own high-level goals were achieved. The authors in [25] argue that intervention is not perceived as such if it does not follow adversary goals.…”

GA‐BPNN Based Hybrid Steering Control Approach for Unmanned Driving Electric Vehicle with In‐Wheel Motors

“…GPS and INS are used to calculate the driving position, vehicle speed, and other information of the unmanned driving vehicle. The navigation parameters are analyzed to guide the unmanned driving IWM-EV to operate along the selected route accurately and safely [20][21][22][23].…”

Hazard-evaluation-oriented moving horizon parallel steering control for driver-automation collaboration during automated driving

“…W ITH the rapid development of parallel control and management theory and its numerous applications in transport automation and vehicle intelligence over the past decade [1], [2], parallel steering control, which is an aspect of parallel driving, has been steadily developed and applied in practice [3], [4]. Moreover, furthered by emerging developments in connected and automated vehicles [5], [6], parallel steering control has become a hot topic and has been garnering increased attention from both academic and industrial researchers [7].…”