## 2 Related Works  
### Input Interactions  
Previous AV studies have investigated the use of touch panels for drivers to select a suggested proposition (e.g., pass the car ahead or hand over driving control) when the automated system has reached its limit [14; 22], or to select AV maneuvers (e.g., change lanes) via touch input implemented on the steering wheel [23]. However, these approaches require the driver's hands to be free to interact with the AV--a less-than-ideal condition when drivers engage in NDRTs--and close enough to the steering wheel to reach the touch panel. Some participants also disliked interacting via touch input which required they divert their gaze from the road to the touch interface while interacting [14]. In addition to touch interaction, hand gesture has been applied for maneuver-based intervention [12] and to control in-car lateral and longitudinal AV motions [24]. While hand-gesture has shown good usability as a remote input interaction, problems with overlapping commands, rates of misrecognition, and arm fatigue diminish its usefulness, and drivers struggle to remember each gesture [12; 24]. Furthermore, hand gesture input may require the driver to stop performing an NDRT to interact with the AV. Voice and gaze inputs can supplement the drawbacks of touch or hand gesture inputs by supporting remote and hands-free input interactions. Voice input control, one of the most common input interactions in AVs, has been implemented to support driver-vehicle cooperation and to select vehicle maneuvers [12; 14]. In previous studies, drivers preferred voice over touch input and hand gestures, as voice commands do not require physical movement and allows them to keep their eyes on the road during hands-free interaction. However, voice input may not work well in a noisy environment (e.g., a conversation with other people in the vehicle), and drivers do not completely trust speech recognition or may be confused about the appropriate command to initiate a desired action [12; 14]. Gaze input interaction has been shown to be faster and more precise than hand gestures [25], and tracking eye movement has been shown to accurately indicate user attention [26; 27]. However, to confirm or complete a selection via gaze, users must dwell for a few seconds on an object [28; 29]. This increases cognitive load and causes eye fatigue [30], obvious problems during driving and limitations for gaze input as a single modality. To eliminate the drawbacks of using gaze input as a single input modality, a gaze combined multimodal interaction has been suggested [26], with gaze determining the object and the combined modality to trigger the selection. This study [15], showed that gaze-touch and gaze-gesture [31] inputs can be used to select objects on a head-up display (HUD) while interacting with in-vehicle infotainment systems. Similarly, researchers in [32] presented a gaze-voice input, where gaze was used to activate the voice input. However, these combined inputs still suffer from the limitations of the voice, touch and gesture single modality inputs mentioned above. Combining gaze with head movement may address most of the aforementioned limitations of a single modality [28; 33; 34]. In gaze-head input interaction, gaze is used to select an object and a head nod is used to trigger the selection [28; 35], where head movement can be defined by orientation angles of the head (roll, pitch, and yaw) [36]. G-HI is natural and intuitive, as it reflects human-to-human interaction and is viable using state-of-the-art eye trackers [25]. In previous studies in augmented reality [37], virtual reality [25; 38], or even collaborative gaming [39], G-HI has achieved good usability and high accuracy, as it supports hands-free input and does not require users to reposition themselves with high accuracy [40]. It has also been shown to be a fast, natural, convenient and accurate way to interact with mobile screens and desktop computing [35; 41].