Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Abstract

This paper assessed four types of human-machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. ["A model for types and levels of human interaction with automation," IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286-297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19-36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisitionlow), sphere (information acquisition-high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers' initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

“…The most common thresholds are 2° for steering and a threshold of 10% actuation from braking (Gold et al, 2017;Louw, Markkula, et al, 2017;Zeeb et al, 2015). Other temporal measures of takeover performance include the time between the warning (or failure) and the redirection of the driver's gaze (Eriksson et al, 2019), repositioning of the hands or feet to the controls (Petermeijer, Bazilinskyy, Begnler, & de Winter, 2017;Petermeijer, Cieler, & de Winter, 2017;Petermeijer, Doubek, & de Winter, 2017), automation deactivation (Dogan et al, 2017;Vogelpohl, Kühn, Hummel, Gehlert, & Vollrath, 2018), or the initiation of the last evasive action (Louw, Markkula, et al, 2017). Table 4 summarizes these measures and their link to driver behaviors.…”

“…Ecological alerts, shown in the right side of Figure 5, describe the features of the situation or provide some instruction to the driver. Auditory (Forster et al, 2017;Walch et al, 2015;Wright et al, 2017aWright et al, , 2017b, visual (Eriksson et al, 2019;Lorenz et al, 2014;Walch et al, 2015), and haptic (Melcher et al, 2015) alerts have been explored in this context. Parallel research has also explored real-time communication of automation uncertainty (Beller, Heesen, & Vollrath, 2013).…”

Predicting driver takeover performance in conditionally automated driving

“…Takeover request (TOR) lead time is the critical event onset for automation failures at the time of the TOR (McDonald et al, 2019). According to the complexity of driving environment and vehicle sensor capability, commonly used TOR lead times range from 1 to s (Eriksson et al, 2018). Research has demonstrated that shorter TOR lead time degraded takeover quality, as demonstrated by higher crash rates, greater maximum accelerations and greater standard deviation of steering wheel angle (Mok et al, 2015;van den Beukel & van der Voort, 2013;Wan & Wu, 2018).…”

Driving Performance After Self-Regulated Control Transitions in Highly Automated Vehicles

“…al., 2013; Merat et al, 2014; Russell et al, 2016). Eriksson et al (in press) showed that when the reason for a TOR was highlighted through an augmented reality overlay, drivers exhibited opportunistic control by braking to buy time. This was not observed when the augmented reality display showed higher levels of semantics, such as arrows indicating safe paths, indicating a higher level of control.…”