Human-Vehicle Cooperation on Prediction-Level: Enhancing Automated Driving with Human Foresight

TL;DR

This paper presents a method for human drivers to augment autonomous vehicle scene predictions via gaze, improving foresight, trust, and comfort in automated driving through an intuitive interface and cooperative prediction approach.

Contribution

It introduces a novel human-in-the-loop approach allowing drivers to supplement scene predictions with gaze, enhancing autonomous driving safety and trustworthiness.

Findings

Feasibility demonstrated in a driving simulator

Enhanced trust and explainability through GUI

Potential for improved foresighted driving abilities

Abstract

To maximize safety and driving comfort, autonomous driving systems can benefit from implementing foresighted action choices that take different potential scenario developments into account. While artificial scene prediction methods are making fast progress, an attentive human driver may still be able to identify relevant contextual features which are not adequately considered by the system or for which the human driver may have a lack of trust into the system's capabilities to treat them appropriately. We implement an approach that lets a human driver quickly and intuitively supplement scene predictions to an autonomous driving system by gaze. We illustrate the feasibility of this approach in an existing autonomous driving system running a variety of scenarios in a simulator. Furthermore, a Graphical User Interface (GUI) was designed and integrated to enhance the trust and explainability of the system. The utilization of such cooperatively augmented scenario predictions has the potential to improve a system's foresighted driving abilities and make autonomous driving more trustable, comfortable and personalized.