A Bayesian Regression Analysis of the Effects of Alert Presence and Scenario Criticality on Automated Vehicle Takeover Performance

Abstract

Objective This study investigates the impact of silent and alerted failures on driver performance across two levels of scenario criticality during automated vehicle transitions of control. Background Recent analyses of automated vehicle crashes show that many crashes occur after a transition of control or a silent automation failure. A substantial amount of research has been dedicated to investigating the impact of various factors on drivers’ responses, but silent failures and their interactions with scenario criticality are understudied. Method A driving simulator study was conducted comparing scenario criticality, alert presence, and two driving scenarios. Bayesian regression models and Fisher’s exact tests were used to investigate the impact of alert and scenario criticality on takeover performance. Results The results show that silent failures increase takeover times and the intensity of posttakeover maximum accelerations and decrease the posttakeover minimum time-to-collision. While the predicted average impact of silent failures on takeover time was practically low, the effects on minimum time-to-collision and maximum accelerations were safety-significant. The analysis of posttakeover control interaction effects shows that the effect of alert presence differs by the scenario criticality Conclusion Although the impact of the absence of an alert on takeover performance was less than that of scenario criticality, silent failures seem to play a substantial role—by leading to an unsafe maneuver—in critical automated vehicle takeovers. Application Understanding the implications of silent failure on driver’s takeover performance can benefit the assessment of automated vehicles’ safety and provide guidance for fail-safe system designs.

Predicting Driver Errors during Automated Vehicle Takeovers

“…Perhaps the most relevant feature in relation to the findings of this study—in particular with regard to the minimum TTC—is the takeover time budget found in Du et al ( 5 ), which was identified as one of the important features in takeover performance prediction. Previous studies have shown that a shorter takeover time budget is associated with a shorter minimum TTC ( 11 ). Thus, our finding is aligned with the takeover time budget found in Du et al ( 5 ).…”

“…The simulator’s automated driving system was activated with a button on a touch screen display located to the right of the steering wheel. When the system encountered a failure or an operational limit, the vehicle’s automated system was disabled (see Alambeigi and McDonald ( 11 ) for a detailed description).…”

“…Participants were calibrated to the FOVIO system using a four-point calibration screen and were instructed to look at the exterior edges of the panoramic display while maintaining a directly forward field of view. The driving performance dataset compiled from the experiment has been published in the Virginia Tech Transportation Institute data repository ( 12 ).…”

Driver Response to Take-Over Requests in Real Traffic

“…These probability distributions can be used by system designers to select values to match their priorities and understand the uncertainty in their decisions. For example, if a TOR is designed to be issued 7 s prior to exiting the ODD, we cannot be sure that all drivers will manage to deactivate automation in that time, as the 95% HPD for the 95th percentile driver who is looking towards an NDRT item when the TOR is issued ranges between 5.1 s and 8.0 s. Despite the advantages of the Bayesian framework, it is rarely used in the literature on human factors of automated driving, with a few exceptions (e.g., [26], [27], [28]).…”

The notorious B.I.T: The effects of a ransomware and a screen failure on distraction in automated driving

“…SAE level 2 automated cars partially relieve individuals from the driving task. Silent failures in these automated vehicles lead to decreased engagement in monitoring the road, poorer take-over performance (Alambeigi et al, 2021;Louw et al, 2019;Mole et al, 2020) and delayed brake response times (Bianchi-Piccinini et al, 2020). Explicit failures, as opposed to silent ones, are notified to the users.…”