Abstract

As semiautonomous driving systems are becoming prevalent in late model vehicles, it is important to understand how such systems affect driver attention. This study investigated whether measures from low-cost devices monitoring peripheral physiological state were comparable to standard EEG in predicting lapses in attention to system failures. Twenty-five participants were equipped with a low-fidelity eye-tracker and heart rate monitor and with a high-fidelity NuAmps 32-channel quick-gel EEG system and asked to detect the presence of potential system failure while engaged in a fully autonomous lane changing driving task. To encourage participant attention to the road and to assess engagement in the lane changing task, participants were required to: (a) answer questions about that task; and (b) keep a running count of the type and number of billboards presented throughout the driving task. Linear mixed effects analyses were conducted to model the latency of responses reaction time (RT) to automation signals using the physiological metrics and time period. Alpha-band activity at the midline parietal region in conjunction with heart rate variability (HRV) was important in modeling RT over time. Results suggest that current low-fidelity technologies are not sensitive enough by themselves to reliably model RT to critical signals. However, that HRV interacted with EEG to significantly model RT points to the importance of further developing heart rate metrics for use in environments where it is not practical to use EEG.

Highlights

  • Semiautonomous driving systems or ‘‘partial driving automation’’ (SAE Level 2; SAE International, 2016) are driver assistance systems that are increasingly available in passenger vehicles, with conditional driving automation (SAE level 3) still largely under development

  • A likelihood ratio test (LRT) comparing the interactive model to a null additive model produced a significant Chi-square (X2(1) = 5.251, p = 0.0219), suggesting that the interaction was important in modeling reaction time (RT)

  • Considered together, these results indicate that alpha-band, heart rate variability (HRV), and time period are important factors in modeling RT, with meanRR a weaker factor

Read more

Summary

Introduction

Semiautonomous driving systems or ‘‘partial driving automation’’ (SAE Level 2; SAE International, 2016) are driver assistance systems that are increasingly available in passenger vehicles, with conditional driving automation (SAE level 3) still largely under development. As recently pointed out by Eriksson and Stanton (2017), SAE level 2 is commonly confused with highly automated driving, when the semiautonomous level requires drivers to monitor the automation. For both SAE levels 2 and 3, drivers must be prepared to intervene when system limitations and failures. These systems are intended to be advanced driver assistance systems (ADASs) and are not intended to supplant the need for drivers to maintain vigilant attention and intervene when necessary. Automatic emergency braking reduced rear-end crashes by about 40% (Cicchino, 2017) and rear cross-traffic alerts reduced backing crashes by about 32% (Cicchino, 2018)

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call