Situational Awareness and Systems for Driver-Assistance

Low level of driver’s situation awareness (SA) and high level of cognitive load are considered as reasons of vehicle accidents. Cognitive load is higher when driving abroad because of unfamiliarity with differences in international traffic rules or vehicle configurations. This paper aims to objectively assess the driver’s SA when performing lane changing tasks under unfamiliar driving conditions. We conducted an experiment using a right-hand driving simulator and a left-hand simulated traffic scenario to collect the temporal information about SA such as time, location, and speed as well as lane changing errors. Overall, the participants show low SA in curved roads and road networks, but high SA in straight roads. The results state that speed does not affect the lane changing performance on straight roads and road networks but significantly affects the lane changing performance on curved roads. These findings can be used to design a SA system for driver-assistance in unfamiliar driving conditions considering drivers’ cognitive load

Introduction to Human- and Task-Centered Assistance Systems Minitrack

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Investigating the Impacts of Connected Vehicles on Driving Aggressiveness and Situational Awareness in Highway Crash Scenarios: A Driving Simulator Study

Psychological factors such as aggressiveness and situational awareness can impact driving performance. Connected vehicles (CV), equipped with advanced sensors and able to communicate safety messages to drivers, have the potential to influence driving performance by altering drivers’ aggressiveness and situational awareness. This paper aims to investigate the impacts of the CVs on driving aggressiveness and situational awareness in highway crash scenarios where a primary crash has already occurred, and a second crash may occur as a result. To achieve this goal, a driving simulator experiment was conducted, and questionnaires focused on driving aggressiveness and CV effectiveness were distributed. Structural equation modeling (SEM) was used to examine the interrelationships between the use of CV alerts, psychological factors, driving behavior, and other factors. Two latent psychological factors were constructed in the SEM, namely, aggressiveness and unawareness, which were measured by statistical measures of speed, longitudinal acceleration, steering angle, brake, yaw, and lane offset while passing the crash scenes. The SEM has the advantage of achieving the measurement of latent psychological factors and interrelationship modeling simultaneously in one statistical estimation procedure. Results showed that the proposed CV alerts significantly improved aggressiveness and situational awareness. These findings provide insights into the development of driving assistance systems that take psychological factors into account.https://digitalcommons.odu.edu/gradposters2023_engineering/1002/thumbnail.jp

Towards a Driving Training System to Support Cognitive Flexibility

Driving under unfamiliar conditions, such as unfamiliar traffic system and unfamiliar vehicle configuration during overseas holidays, might cause fatality, injury or property damage. In these cases, a driver needs to apply their prior knowledge to a new driving situation in order to drive safely. This ability is called cognitive flexibility. Prior research has found that left/mixed-handed people show superior cognitive flexibility in tasks required such ability than right-handed people. This paper aims to explore the relationships among cognitive flexibility, handedness and the types of errors drivers make, specifically at roundabouts and intersections in an unfamiliar driving condition. We conducted an experiment using a right-hand driving simulator and a left-hand simulated traffic scenario as a driving condition to collect the related data to driving at roundabout and intersection. All participants were not familiar with that condition. We found that left/mixed-handed drivers show a significantly superior cognitive flexibility at a turn-left roundabout and intersection. Also left/mixed handed drivers make a significantly fewer number of errors than right-handed drivers when entering the roundabout and approaching the intersection

Age Differences in the Situation Awareness and Takeover Performance in a Semi-Autonomous Vehicle Simulator

Research on young and elderly drivers indicates a high crash risk amongst these drivers in comparison to other age groups of drivers. Young drivers have a greater propensity to adopt a risky driving style and behaviors associated with poor road safety. On the other hand, age-related declines can negatively impact the performance of older drivers on the road leading to crashes and risky maneuvers. Thus, autonomous vehicles have been suggested to improve the road safety and mobility of younger and older drivers. However, the difficulty of manually taking over control from semi-autonomous vehicles might vary in different driving conditions, particularly in those that are more challenging. Hence, the present study aims to examine the effect of road geometry and scenario, by investigating young, middle-aged and older drivers' situation awareness (SA) and takeover performance when driving a semi-autonomous vehicle simulator on a straight versus a curved road on a highway and an urban non-highway road when engaged in a secondary distracting task. Due to the impact of COVID-19, data from only the young (n=24) and middle-aged (n=24) adults were collected and analyzed. Participants drove a Level 3 semi-autonomous simulator vehicle and performed a secondary non-driving related task in the distracted conditions. The results indicated that the participants had significantly longer hazard perception times on the curved roads and autopilot drives, but there was no significant effect of driver age and road type. Their Situation Awareness Global Assessment Technique (SAGAT) scores were higher in the highway scenarios, on the straight roads, and in the manual drive compared to the autopilot with distraction drive. Young drivers were also found to have significantly higher SAGAT scores than middle-aged drivers. While there was a significant interaction effect between road type and road geometry on takeover time, there was no significant main effect of road geometry, drive type and driver’s age. For the takeover quality metrics, road geometry and drive type had an effect on takeover performance. The resulting acceleration was higher for the straight road and in the autopilot drives, and the lane deviation was higher on the curved road and autopilot only drive compared to the autopilot with distraction drive. There was no significant main effect of road type and driver’s age on resulting acceleration and lane deviation. Overall, while there were age differences in some aspects of SA, young and middle-aged drivers did not differ in their takeover performance. The participants' SA was impacted by the road type and geometry and their takeover quality varied according to the road geometry and drive type. The outcomes of this research will aid vehicle manufacturing companies that are developing Level 3 semi-autonomous vehicles with appropriately designing the lead time of the takeover request to meet the driving style and abilities of younger and middle-aged drivers. This will also help to improve road safety by reducing the crash rate of younger drivers