Measuring Drivers’ Physiological Response to Different Vehicle Controllers in Highly Automated Driving (HAD): Opportunities for Establishing Real-Time Values of Driver Discomfort

This study investigated how driver discomfort was influenced by different types of automated vehicle (AV) controllers, compared to manual driving, and whether this response changed in different road environments, using heart-rate variability (HRV) and electrodermal activity (EDA). A total of 24 drivers were subjected to manual driving and four AV controllers: two modelled to depict “human-like” driving behaviour, one conventional lane-keeping assist controller, and a replay of their own manual drive. Each drive lasted for ~15 min and consisted of rural and urban environments, which differed in terms of average speed, road geometry and road-based furniture. Drivers showed higher skin conductance response (SCR) and lower HRV during manual driving, compared to the automated drives. There were no significant differences in discomfort between the AV controllers. SCRs and subjective discomfort ratings showed significantly higher discomfort in the faster rural environments, when compared to the urban environments. Our results suggest that SCR values are more sensitive than HRV-based measures to continuously evolving situations that induce discomfort. Further research may be warranted in investigating the value of this metric in assessing real-time driver discomfort levels, which may help improve acceptance of AV controllers

Is Users’ Trust during Automated Driving Different When Using an Ambient Light HMI, Compared to an Auditory HMI?

The aim of this study was to compare the success of two different Human Machine Interfaces (HMIs) in attracting drivers’ attention when they were engaged in a Non-Driving-Related Task (NDRT) during SAE Level 3 driving. We also assessed the value of each on drivers’ perceived safety and trust. A driving simulator experiment was used to investigate drivers’ response to a non-safety-critical transition of control and five cut-in events (one hard; deceleration of 2.4 m/s2, and 4 subtle; deceleration of ~1.16 m/s2) over the course of the automated drive. The experiment used two types of HMI to trigger a takeover request (TOR): one Light-band display that flashed whenever the drivers needed to takeover control; and one auditory warning. Results showed that drivers’ levels of trust in automation were similar for both HMI conditions, in all scenarios, except during a hard cut-in event. Regarding the HMI’s capabilities to support a takeover process, the study found no differences in drivers’ takeover performance or overall gaze distribution. However, with the Light-band HMI, drivers were more likely to focus their attention to the road centre first after a takeover request. Although a high proportion of glances towards the dashboard of the vehicle was seen for both HMIs during the takeover process, the value of these ambient lighting signals for conveying automation status and takeover messages may be useful to help drivers direct their visual attention to the most suitable area after a takeover, such as the forward roadway

Hand-held cell phone use while driving legislation and observed driver behavior among population sub-groups in the United States

Abstract Background Cell phone use behaviors are known to vary across demographic sub-groups and geographic locations. This study examined whether universal hand-held calling while driving bans were associated with lower road-side observed hand-held cell phone conversations across drivers of different ages (16–24, 25–59, ≥60 years), sexes, races (White, African American, or other), ruralities (suburban, rural, or urban), and regions (Northeast, Midwest, South, and West). Methods Data from the 2008–2013 National Occupant Protection Use Survey were merged with states’ cell phone use while driving legislation. The exposure was presence of a universal hand-held cell phone ban at time of observation. Logistic regression was used to assess the odds of drivers having a hand-held cell phone conversation. Sub-groups differences were assessed using models with interaction terms. Results When universal hand-held cell phone bans were effective, hand-held cell phone conversations were lower across all driver demographic sub-groups and regions. Sub-group differences existed among the sexes (p-value, <0.0001) and regions (p-value, 0.0003). Compared to states without universal hand-held cell phone bans, the adjusted odds ratio (aOR) of a driver hand-held phone conversation was 0.34 [95% confidence interval (CI): 0.28, 0.41] for females versus 0.47 (CI 0.40, 0.55) for males and 0.31 (CI 0.25, 0.38) for drivers in Western states compared to 0.47 (CI 0.30, 0.72) in the Northeast and 0.50 (CI 0.38, 0.66) in the South. Conclusions The presence of universal hand-held cell phone bans were associated lower hand-held cell phone conversations across all driver sub-groups and regions. Hand-held phone conversations were particularly lower among female drivers and those from Western states when these bans were in effect. Public health interventions concerning hand-held cell phone use while driving could reasonably target all drivers

Finger millet blast disease management: a key entry point for fighting malnutrition and poverty in East Africa

Finger millet is a staple, high-quality food, important to the livelihoods of millions of smallholder farmers in East Africa. It has been neglected by major donors to agricultural research. This paper reports recent investment by the UK Department for International Development (DFID) in several projects on blast disease that has not only led to successful promotion of sound blast management strategies to farmers, but has also fostered partnerships in an evolving finger millet innovation system in East Africa. A key entry point has been created to address other constraints to finger millet production and utilization, such as ineffective weed management, poor grain quality, inefficient seed systems and production-supply chain problems, notably through 'spill-in' and adaptation of relevant technologies developed elsewhere. Further donor investment in the finger millet sector is likely to make a significant contribution to fighting malnutrition and poverty in East Africa

Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation (SAE Level 3) or monitored the drive (SAE Level 2). Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ∼ 18 min each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. Results showed that driver workload due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that a lead vehicle maintain a shorter THW can significantly increase driver workload during takeover scenarios, potentially affecting driver safety. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional cognitive or attentional demands on the driver. Our results indicated that ECG and EDA signals are sensitive to variations in workload, which warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help future driver monitoring systems respond appropriately to the limitations of the driver, and predict their performance in the driving task, if and when they have to resume manual control of the vehicle after a period of automated driving