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

Abstract

Objective: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. Background: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. Method: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. Results: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. Conclusion: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. Application: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

Predicting takeover response to silent automated vehicle failures

“…In other words, there will be a "silent failure", and it will be up to the supervising driver to detect that the AV has failed and then to respond safely to the conditions. Throughout this manuscript situations where the AV fails without providing any explicit alert to the driver will be referred to as silent failures (as per [3,4]). Human detection of these silent failures in automated lane keeping, the resultant steering responses when regaining control, and how distraction affects these behaviours, will be the focus of this manuscript.…”

“…Understanding how humans respond to both planned takeover and silent failure conditions will be crucial to setting safety boundaries of AVs. The considerable research examining planned takeover requests allows manufacturers and legislators to design systems and regulations that support safe AVs (for reviews see [3,5,6]). However, adequate safety boundaries cannot be established until researchers can predict with confidence how humans respond to silent failures that could, hypothetically, occur at any point during automated driving.…”

“…Dinparastdjadid et al [10] showed that a popular model of manual steering control, where drivers generate control outputs based on a weighted combination of angular inputs from a near and a far point [11], can capture the lane position and orientation profiles of steering recoveries to silent failures (where the vehicle drifted without warning while the driver was looking towards a visual distraction task) but crucially fails to describe how the driver moves the steering wheel. Further development is clearly needed for models to capture the mechanisms underpinning steering behaviour in silent failures [3,10].…”

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

“…Therefore, the effects on traffic flow forecasted by these models could be unrealistic. The behavioural realism of the mathematical models available can be improved by incorporating findings from human factors and driver psychology (Saifuzzaman and Zheng 2014;Hamdar, Mahmassani, and Treiber 2015;Hess 2018, 2019a;Van Lint and Calvert 2018;Hamdar et al 2019;McDonald et al 2019).…”

HCF: A Hybrid CNN Framework for Behavior Detection of Distracted Drivers

“…For example, distracted driving can cause wrong lane changes, which will lead to serious traffic accidents [4] [5]. According to the preliminary definition of the International Organization for Standardization (ISO) [6], distracted driving is "attention given to a non-driving related activity, typically to the detriment of driving performance" The National Highway Traffic Safety Administration (NHTSA) defines distracted driving as "any activity that diverts attention from driving, including talking or texting on the phone, eating and drinking, etc." [7].…”