Drivers’ hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25–50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver’s hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver’s hazardous state, which could serve as the basis for more intelligent intervention systems.

In 2015, the U.S. Department of Transportation (U.S. DOT) selected Wyoming as one of three sites to develop, test, and deploy a suite of connected vehicle (CV) applications on a 402-mi Interstate 80 corridor. One of the Wyoming’s key CV applications is the variable speed limit (VSL) warning, which aimed to provide commercial truck drivers with real-time regulatory and advisory speed limits to help in better managing speeds under adverse weather conditions, and reducing potential speed variances that may cause traffic collisions. This paper developed a driving simulator testbed to assess the impact of the Wyoming’s CV-based VSL (CV-VSL) application on truck drivers’ behavior under adverse weather conditions. A total of 18 professional truck drivers were recruited to participate in the driving simulator experiment. Participants’ instantaneous speeds at various locations were collected to reveal the impact of the CV-VSL warnings on their driving behavior. Simulation results showed that when the advisory speed limits were lower than 55 mph, participants generally followed the VSLs displayed on the CV human–machine interface (HMI). In addition, traffic flows utilizing CV-VSL technology tend to exhibit lower average speeds and speed variances compared with baseline scenarios. These effects of CV-VSL warnings can bring potential safety benefits, as reduction in average speeds and speed variances are effective surrogate measures of safety, that is, lower risk of crashes, under adverse weather conditions.

Autonomous vehicles (AV) are being widely tested around the world. California is one of the jurisdictions permitting extensive AV testing. Field testing resulted in approximately 0.02 AV crashes per 1,000 AV miles traveled, as well as incidents leading to driver disengagement of the AV systems. Factors related to human error, system failure, surrounding vehicles, and roadway features could cause an AV-involved crash to occur or result in pre-crash disengagement. This study focuses on AV crashes in which the AV was operating in active AV mode or shortly after the test operator had disengaged the AV to resume conventional control. AV crash data were extracted for the years 2017 to 2021 from California’s AV crash database maintained by the California Department of Motor Vehicles (DMV) to account for the rapid pace of AV development and focus on current causes of incidents. This paper utilized multiple statistical approaches to quantitatively investigate AV crashes and disengagement events. Investigation of latent manifest using exploratory factor analysis (EFA) was conducted and ordinal logit models and decision trees were employed in this study. EFA clarified latent variables that could identify an AV crash, in which operator involvement, incorrect maneuver decision, crash severity, and environmental conditions were the manifests obtained from the analysis. Results from the logistic regression and the decision trees showed that collision type, AV movement type, and other vehicle movement type are significant factors contributing to AV crashes.

Traffic crashes can be divided into primary and secondary crashes. Secondary crashes occur as a consequence of primary crashes within their spatiotemporal distances. Secondary crashes comprise nearly 20% of all crashes and 18% of fatal crashes, in which they can possibly have a higher crash severity than the primary crash. Interstate-80 in Wyoming is a major rural corridor with a high freight traffic volumes. The Federal Highway Administration selected Wyoming in which to deploy connected vehicle (CV) technology with a focus on commercial truck safety. Distress and rerouting applications were among the suite of CV pilot applications. Very few studies have investigated the safety performance of CVs in mitigating the risk of secondary crashes on heavy trucks, more specifically under adverse weather conditions. This study filled this gap by conducting a driving simulator experiment to assess the effectiveness of CV distress and rerouting applications in mitigating the effects of secondary crashes. A total of 23 truck drivers were recruited to this study. The analysis was conducted on the vehicle kinematics obtained from the driving simulator. A CV and a nonCV scenario were designed to compare the participants’ driving behavior under adverse weather conditions. The results showed that the tested CV applications succeeded in enhancing driving behaviors by reducing the operating speed as well as the speed variation, and all the participants avoided a secondary crash in the CV environment. In addition, the distress notification coupled with the road closure reduced the average operating speed by 26% from the provided speed limit.