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Putting the Brain in the Driver’s Seat: Using Transcranial Doppler Sonography to Examine Vigilance in Automated Driving

Although automated vehicles may help improve driver safety by maintaining speed and lane position (SAE, 2018), their automated systems are imperfect. As a result, drivers are responsible for supervising the automation and are expected to intervene as needed (e.g., detecting roadway obstacles when the automated system fails to do so). Failure to effectively monitor the automated system can cause collisions, which may be caused by inadequate driver vigilance. Vigilance is an individual’s ability to sustain attention to detect rare and unpredictable signals for a prolonged time (Warm et al. 2008). In the case of automated driving, the driver is responsible for monitoring the vehicle and the roadway while watching for events that could indicate an imminent automation failure (e.g., unusual roadway obstacles; SAE, 2018). Recent research found that drivers’ ability to discriminate hazardous and safe roadway events declined in a partially automated vehicle, but not in a manually driven vehicle – suggesting that automated vehicle drivers may be especially susceptible to a decrement (Greenlee et al., 2022). Resource theory provides a possible explanation for the relatively severe vigilance decrement in automated vehicles. Resource theory states that the vigilance decrement occurs because individuals have a limited capacity of cognitive information-processing resources that are depleted over time, resulting in insufficient resources to maintain vigilance (Warm et al., 2008). Resource theory also argues that more demanding vigilance tasks are likely to induce poorer vigilance performance and more severe vigilance decrements (Warm et al., 2008). Thus, driver vigilance may be poorer in automated driving than in manual driving because automated driving is more cognitively demanding. The current study examined this possibility using Transcranial Doppler Sonography (TCD), a measure of cerebral blood flow velocity (CBFV). CBFV is an index of cognitive resource utilization and is reflective of vigilance task performance. More difficult vigilance tasks are associated with elevated CBFV, suggesting higher resource requirements; and temporal declines in performance are associated with declines in CBFV, suggesting resources are depleted over time (Warm & Parasuraman, 2007). This study also investigated whether elevated task demands (i.e., faster event rate) would differentially impact detection performance and CBFV in manual and automated driving. If the demands of automated driving are greater than those of manual driving, then further elevation of task demands would be expected to increase the potential for overload-induced resource depletion. Forty-four individuals were randomly assigned to one of four conditions: 1) manual driving with a slow event rate, 2) manual driving with a fast event rate, 3) automated driving with a slow event rate, 4) automated driving with a fast event rate. Manual driving participants controlled speed and lane position. Automated driving participants did not need to control speed or lane position. Event rate was either slow (15 events/min) or fast (30 events/min). All participants were equipped with the TCD and asked to detect unsafe vehicles that partially obstructed the roadway. Results revealed that hazard detection performance declined over the time for the automated drive regardless of event rate, whereas performance in the manual drive only declined when event rate was fast. Left hemisphere CBFV declined in all conditions and was greater in the automated driving condition than in the manual driving condition. Right hemisphere CBFV was greater in the fast event rate condition than in the slow event rate condition, and it declined selectively in the fast event rate condition. Resource theory provides a potential explanation for the divergence between manual and automated detection results. It may be that manual driving with a slow event rate is relatively non-demanding which prevents a performance decrement. However, by increasing event rate in the manual driving condition, the need for attentional resources is increased, leading to overload, depletion, and a performance decrement. Unlike in the manual driving condition, the slow event rate does not protect against the vigilance decrement within the automated driving condition. This suggests that attentional demands of monitoring in automated driving may be elevated, relative to manual driving – leading to degraded vigilance and poorer hazard detection performance outcomes in automated driving. This conclusion is supported by the fact that left hemisphere CBFV was elevated in the automated driving condition compared to the manual driving condition, suggesting that the demand for neurocognitive processing resources was greater in automated driving. The right hemisphere CBFV results provide confirmatory evidence that the high event rate condition was more demanding and likely to induce resource depletion than the low event rate. Overall, the present results demonstrate that vehicle automation negatively impacts performance and cognitive resource utilization. The current results also indicate that neurophysiological assessment of cognitive load could be useful in the continued development of automated vehicles.