Human-robot shared autonomy techniques for vehicle navigation hold promise for reducing a human driver’s workload, ensuring safety, and improving navigation efficiency. However, because typical techniques achieve these improvements by effectively removing human control at critical moments, these approaches often exhibit poor responsiveness to human commands—especially in cluttered environments. In this paper, we propose a novel goal-blending shared autonomy (GBSA) system, which aims to improve responsiveness in shared autonomy systems by blending human and robot input during the selection of local navigation goals as opposed to low-level motor (servo-level) commands. We validate the proposed approach by performing a human study involving an intelligent wheelchair and compare GBSA to a representative servo-level shared control system that uses a policy-blending approach. The results of both quantitative performance analysis and a subjective survey show that GBSA exhibits significantly better system responsiveness and induces higher user satisfaction than the existing approach.

Motivated by the desire to give vehicles better information about their drivers, we explore human intent inference in the setting of a human driver riding in a moving vehicle. Specifically, we consider scenarios in which the driver intends to go to or learn about a specific point of interest along the vehicle's route, and an autonomous system is tasked with inferring this point of interest using gaze cues. Because the scene under observation is highly dynamic --- both the background and objects in the scene move independently relative to the driver --- such scenarios are significantly different from the static scenes considered by most literature in the eye tracking community. In this paper, we provide a formulation for this new problem of determining a point of interest in a dynamic scenario. We design an experimental framework to systematically evaluate initial solutions to this novel problem, and we propose our own solution called dynamic interest point detection (DIPD). We experimentally demonstrate the success of DIPD when compared to baseline nearest-neighbor or filtering approaches.

In this paper, we introduce the problem of enroute destination changing for a self-driving car, and we study the effectiveness of human-robot copilot systems as a solution. The copilot system is one in which the autonomous vehicle not only handles low-level vehicle control, but also continually monitors the intent of the human passenger in order to respond to dynamic changes in desired destination. We specifically consider a vehicle parking task, where the vehicle must respond to the user's intent to drive to and park next to a particular roadside sign board, and we study a copilot system that detects the passenger's intended destination based on gaze. We conduct a human study to investigate, in the context of our parking task, (a) if there is benefit in using a copilot system over manual driving, and (b) if copilot systems that use eye tracking to detect the intended destination have any benefit compared to those that use a more traditional, keyboard-based system. We find that the answers to both of these questions are affirmative: our copilot systems can complete the autonomous parking task more efficiently than human drivers can, and our copilot system that utilizes gaze information enjoys an increased success rate over one that utilizes typed input.