The number of automated features in surface vehicles are increasing as new vehicles are released each year. Some of these features allow drivers to temporarily take their attention off-road and en-gage in other tasks. However, there are times when it is important for drivers to immediately take control of the vehicle, if required. To safely take control, drivers must understand what is required of them and have situation awareness (SA) to understand important changes or factors within the environment around them. We can present drivers with needed takeover information using a head-up display (HUD), keeping the driver's eyes on the road. However, drivers operating conditionally automated vehicles on various roadways, such as highways and urban arterial roads, require differ-ent information to be conveyed to them as they drive due to inherent differences in roadway and obstacle features within the driving scene, such as the addition of vulnerable road users on urban arterial roads. This work aimed to (1) investigate impacts of novel HUDs on driver situation awareness during takeover on a highway, (2) identify system design criteria to fulfill driver's needs during takeover on an urban arterial road, and, (3) examine the effects of HUDs on driver situation awareness during takeover on an urban arterial road. We investigated these goals by collecting em-pirical data for takeover performance metrics, self-reported situation awareness, participant prefer-ences, and expert's opinions. From our studies we conclude that HUDs can increase aspects of takeover performance on high-ways, with participants demonstrating lower response times and higher time to collision metrics. We did not find significant impacts of HUDs on driver situation awareness on highways. Results from our semistructed interviews indicated that experts felt systems should communicate the need for driver attention to relevant information, communicate obstacle information, and provide information using a variety of driver senses. HUDs can also increase driver situation awareness during takeover on an urban arterial road and support improved takeover performance. This work allowed us to identify potential use cases and design criteria for new designs of novel HUDs to deliver important information during takeover. / Doctor of Philosophy / More features that take some of the tasks of vehicle operation off drivers are being released with every new model year of vehicle. Currently, these features still require drivers to maintain attention to the road and, in some cases, immediately take control of the vehicle, called takeover. However, research has not identified how best to communicate the need for takeover on all types of roads. Research has utilized a head-up display (HUD) to present vehicle information, communicate navigation, and highlight objects around the world to drivers while keeping driver's eyes on road. Keeping driver's eyes on road allows drivers to maintain situation awareness (SA) where they would perceive, understand, and react to changes in the driving scene. Currently, we can convey information to drivers both using traditional head-down displays (HDDs) in the instrument cluster and some vehicles are equipped with HUDs that can deliver in-formation within driver's field of view. This work aimed to first understand how takeover request delivered via HUD affect takeover performance and drivers' situation awareness on highways compared to HDDs. Next, we investigated expert's opinions on driver needs from the automated system during takeover on urban arterial roads to develop design criteria for new types of takeover requests. Finally, we took the design criteria to develop, test, and compare driver's takeover performance and situation awareness in new takeover requests delivered by HDDs and HUDs. HUDs may be useful in presenting information to drivers during takeover. Results support that on highways, HUDs are beneficial for increasing safer driver responses, where they responded quick-er and kept a greater distance to an object in the road in front of them. From design criteria identified by experts, we designed alerts that directed driver's attention to bicyclists, pedestrians, and vehicles crossing the path of their vehicle. After testing the alerts, results indicated that drivers had higher levels of situation awareness and performance metrics during takeover on urban arterial roads. Though HUDs show promise in increasing driver's takeover performance and situation awareness, we must take careful consideration into design of future HUDs to give appropriate and relevant information to drivers.

Researchers have postulated that the implementation of driving automation systems could reduce the prevalence of driver errors, or at least mitigate the severity of their consequences. While driving automation systems are becoming increasingly common on new vehicles, drivers seem to know very little about them. The following dissertation describes an investigation of driver behavior and behavioral adaptation while using driving automation systems in order to improve consumer education and training. This dissertation uses data collected from test track environments and two naturalistic driving studies, the Virginia Connected Corridor 50 (VCC50) Vehicle Naturalistic Driving Study and the NHTSA Level 2 Naturalistic Driving Study (L2 NDS), to investigate driver behavior with driving automation systems and make suggestions for modifications to current consumer education practices. Results from the test track study indicated that while training strategy elicited limited differences in knowledge and no difference in driver behaviors or attitudes, operator behaviors and attitudes were heavily influenced by time and experience with the driving automation. The naturalistic assessment of VCC50 data showed that drivers tended to activate systems more frequently in appropriate roadway environments. However, drivers spent more time looking away from the road while driving automation systems were active and drivers were more likely be observed browsing on their cell phones while using driving automation systems. The analysis of L2 NDS showed that drivers' time gap preferences changes as drivers gain experience using the driving automation systems. Additionally, driver eye glance behavior was significantly different with automation use and indicated the potential for an adaptive trend with increased exposure to the system for both glances away from the roadway and glances to the instrument panel. The penultimate chapter of this work presents training guidelines and recommendations for consumer education with driving automation systems based on this and other research that has been conducted on driver interaction with driving automation systems. The results of this research indicate that driver training should be a key focus in future efforts to ensure the continued safe use of driving automation systems as they continue to emerge in the vehicle fleet. / Doctor of Philosophy / While driving automation systems are becoming increasingly common on new vehicles, drivers seem to know very little about them. Previous studies have found that owners of vehicles equipped with advanced technologies have demonstrated misperceptions or lack of awareness about system limitations, which may impact driver comfort with and reliance on these systems. Partial driving automation systems are designed to assist drivers in some vehicle operation demands, they are not, however, designed to completely remove the driver from the driving task. The following dissertation describes an investigation of driver behavioral adaptation while using driving automation systems with the goal of improving consumer education and training.