IntroductionPartial driving automation is not always reliable and requires that drivers maintain readiness to take over control and manually operate the vehicle. Little is known about differences in drivers’ arousal and cognitive demands under partial automation and how it may make it difficult for drivers to transition from automated to manual modes. This research examined whether there are differences in drivers’ arousal and cognitive demands during manual versus partial automation driving.MethodWe compared arousal (using heart rate) and cognitive demands (using the root mean square of successive differences in normal heartbeats; RMSSD, and Detection Response Task; DRT) while 39 younger (M = 28.82 years) and 32 late-middle-aged (M = 52.72 years) participants drove four partially automated vehicles (Cadillac, Nissan Rogue, Tesla, and Volvo) on interstate highways. If compared to manual driving, drivers’ arousal and cognitive demands were different under partial automation, then corresponding differences in heart rate, RMSSD, and DRT would be expected. Alternatively, if drivers’ arousal and cognitive demands were similar in manual and partially automated driving, no difference in the two driving modes would be expected.ResultsResults suggest no significant differences in heart rate, RMSSD, or DRT reaction time performance between manual and partially automated modes of driving for either younger or late-middle-aged adults across the four test vehicles. A Bayes Factor analysis suggested that heart rate, RMSSD, and DRT data showed extreme evidence in favor of the null hypothesis.ConclusionThis novel study conducted on real roads with a representative sample provides important evidence of no difference in arousal and cognitive demands. Younger and late-middle-aged motorists who are new to partial automation are able to maintain arousal and cognitive demands comparable to manual driving while using the partially automated technology. Drivers who are more experienced with partially automated technology may respond differently than those with limited prior experience.

Semi-automated vehicles (Level-2) provide driving assistance, but they still require driver supervision to maintain safe driving. However, little is known about potential differences in drivers’ cognitive states during manual vs. Level-2 automated driving. The current study systematically examined the effects of manual and Level-2 driving on drivers’ arousal and workload during on-road driving. No differences between the two driving modes were found for the five outcomes that assessed cognitive arousal and workload (i.e., heart rate, root mean square of successive heart period differences, EEG alpha power, and hit rate and reaction time on a secondary task). A Bayes Factor analysis suggested that there is strong evidence that cognitive arousal and workload during Level-2 driving did not differ from manual driving. These novel and theoretically meaningful findings provide strong evidence of similar cognitive arousal and workload states in Level-2 automation and manual driving.

Automated, low-speed shuttles are being deployed to help solve the first-mile/last-mile problem in several cities worldwide. To achieve full automation, each of the roles and responsibilities of the operator must be considered. This research aimed to address how increased ridership, abrupt emergency stops, and the operator influenced the development of trust in riders. Surveys and video footage were collected from riders, as well as the operator on-board. Results suggested that increased ridership with the shuttle predicts more positive experiences and confidence in the technology. However, riders that experienced one or more unexpected emergency stops during shuttle operation were less trusting of the technology. In addition, we found that the backup operator actively worked to foster rider trust. These findings suggest that several challenges will need to be addressed in order to develop and maintain rider trust in low speed automated shuttles when an operator is no longer present.

Source-Separation Non-Negative Matrix Factorization (SSNMF) is a mathematical algorithm recently developed to extract scalp-recorded frequency-following responses (FFRs) from noise. Despite its initial success, the effects of silent intervals on algorithm performance remain undetermined. Our purpose in this study was to determine the effects of silent intervals on the extraction of FFRs, which are electrophysiological responses that are commonly used to evaluate auditory processing and neuroplasticity in the human brain. We used an English vowel /i/ with a rising frequency contour to evoke FFRs in 23 normal-hearing adults. The stimulus had a duration of 150 ms, while the silent interval between the onset of one stimulus and the offset of the next one was also 150 ms. We computed FFR Enhancement and Noise Residue to estimate algorithm performance, while silent intervals were either included (i.e., the WithSI condition) or excluded (i.e., the WithoutSI condition) in our analysis. The FFR Enhancements and Noise Residues obtained in the WithoutSI condition were significantly better ( p < .05) than those obtained in the WithSI condition. On average, the exclusion of silent intervals produced a 11.78% increment in FFR Enhancement and a 20.69% decrement in Noise Residue. These results not only quantify the effects of silent intervals on the extraction of human FFRs, but also provide recommendations for designing and improving the SSNMF algorithm in future research.