FIELD

This disclosure relates generally to evaluating situation awareness of a person, and more particularly, to doing so based on eye tracking.

BACKGROUND

For effective human-machine cooperation in (at least) partially automated driving, both the human driver and the vehicle may be attentive to each other's current states and/or future intentions. It may be important for advanced driving assistance systems (ADAS) to not only correctly analyze the environment they are operating in, but also correctly analyze the current state and/or intentions of a vehicle's operator/driver. For example, it may be beneficial for advanced driving systems to not only monitor and correctly analyze environmental changes of the vehicle, but also to correctly analyze the driver's awareness of these environmental changes (e.g., approaching pedestrians, road hazards, etc.) before providing appropriate assistance or intervention (e.g., collision warning or emergency braking). However, it may be challenging to objectively assess if the driver is aware of a specific road hazard.

SUMMARY

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

According to some aspects of the present disclosure, systems and methods for operationalizing driver eye movement based on moving objects of interest are provided. The disclosed predictive features and models may identify spatial and/or temporal aspects of driver eye glance behavior that strongly correlate to the driver's real time situational awareness relative to one or more road objects of interest. The proposed systems and methods may be further combined with computer-vision techniques such as object recognition to fully automate eye movement data processing as well as machine learning approaches to improve the accuracy of driver awareness estimation.

Some examples of the disclosure are directed to a method including detecting, using one or more sensors, one or more first characteristics associated with a gaze of a person with respect to an object during a period of time. In some examples, the method may include detecting, using the one or more sensors, one or more second characteristics associated with the gaze of the person with respect to the object at a first time after the period of time. In some examples, the method may include determining, using a predictive model, a likelihood that the person is aware of the object based on the one or more first characteristics and the one or more second characteristics. In some examples, the method may include selectively performing an operation based on the determined likelihood.

Some examples of the disclosure are directed to an electronic device including one or more processors, and memory storing instructions, which when executed by the one or more processors, cause the electronic device to perform a method. In some examples, the method includes detecting, using one or more sensors, one or more first characteristics associated with a gaze of a person with respect to an object during a period of time. In some examples, the method includes detecting, using the one or more sensors, one or more second characteristics associated with the gaze of the person with respect to the object at a first time after the period of time. In some examples, the method incudes determining, using a predictive model, a likelihood that the person is aware of the object based on the one or more first characteristics and the one or more second characteristics. In some examples, the method includes selectively performing an operation based on the determined likelihood.

Some examples of the disclosure are directed to a non-transitory computer-readable storage medium including instructions, wherein the instructions are configured to be executed by one or more processors of an electronic device. In some examples, the instructions are for detecting, using one or more sensors, one or more first characteristics associated with a gaze of a person with respect to an object during a period of time. In some examples, the instructions are for detecting, using the one or more sensors, one or more second characteristics associated with the gaze of the person with respect to the object at a first time after the period of time. In some examples, the instructions are for determining, using a predictive model, a likelihood that the person is aware of the object based on the one or more first characteristics and the one or more second characteristics. In some examples, the instructions are for selectively performing an operation based on the determined likelihood.

