Post Take-Over Performance Varies in Drivers of Automated and Connected Vehicle Technology in Near-Miss Scenarios.

OBJECTIVE: This study examined the impact of monitoring instructions when using an automated driving system (ADS) and road obstructions on post take-over performance in near-miss scenarios. BACKGROUND: Past research indicates partial ADS reduces the driver's situation awareness and degrades post take-over performance. Connected vehicle technology may alert drivers to impending hazards in time to safely avoid near-miss events. METHOD: Forty-eight licensed drivers using ADS were randomly assigned to either the active driving or passive driving condition. Participants navigated eight scenarios with or without a visual obstruction in a distributed driving simulator. The experimenter drove the other simulated vehicle to manually cause near-miss events. Participants' mean longitudinal velocity, standard deviation of longitudinal velocity, and mean longitudinal acceleration were measured. RESULTS: Participants in passive ADS group showed greater, and more variable, deceleration rates than those in the active ADS group. Despite a reliable audiovisual warning, participants failed to slow down in the red-light running scenario when the conflict vehicle was occluded. Participant's trust in the automated driving system did not vary between the beginning and end of the experiment. CONCLUSION: Drivers interacting with ADS in a passive manner may continue to show increased and more variable deceleration rates in near-miss scenarios even with reliable connected vehicle technology. Future research may focus on interactive effects of automated and connected driving technologies on drivers' ability to anticipate and safely navigate near-miss scenarios. APPLICATION: Designers of automated and connected vehicle technologies may consider different timing and types of cues to inform the drivers of imminent hazard in high-risk scenarios for near-miss events.

A hierarchical machine learning classification approach for secondary task identification from observed driving behavior data.

According to NHTSA, more than 3477 people (including 551 non-occupants) were killed and 391,000 were injured due to distraction-related crashes in 2015. The distracted driving epidemic has long been under research to identify its impact on driving behavior. There have been a few attempts to detect drivers' engagement in secondary tasks from observed driving behavior. Yet, to the authors' knowledge, not much effort has been directed to identify the types of secondary tasks from driving behavior parameters. This study proposes a bi-level hierarchical classification methodology using machine learning to identify the different types of secondary tasks drivers are engaged in using their driving behavior parameters. At the first level, drivers' engagement in secondary tasks is detected, while at the second level, the distinct types of secondary tasks are identified. Comparative evaluation is performed between nine ensemble tree classification methods to identify three types of secondary tasks (hand-held cellphone calling, cellphone texting, and interaction with an adjacent passenger). The inputs to the models are five driving behavior parameters (speed, longitudinal acceleration, lateral acceleration, pedal position, and yaw rate) along with their standard deviations. The results showed that the overall secondary task detection accuracy ranged from 66% to 96%, except for the Decision Tree that was able to detect engagement in secondary tasks with a high accuracy of 99.8%. For the identification of secondary tasks types, the overall accuracy ranged from 55% to 79%, with the highest accuracy of 82.2% achieved by the Random Forest method. The findings of the paper show the proposed methodology promising to (1) characterize drivers' engagement in unlawful secondary tasks (such as texting) as a counter measure to prevent crashes, and (2) alert drivers to pay attention back to the main driving task when risky changes to their driving behavior take place.

Detection of driver engagement in secondary tasks from observed naturalistic driving behavior.

Distracted driving has long been acknowledged as one of the leading causes of death or injury in roadway crashes. The focus of past research has been mainly on the impact of different causes of distraction on driving behavior. However, only a few studies attempted to address how some driving behavior attributes could be linked to the cause of distraction. In essence, this study takes advantage of the rich SHRP 2 Naturalistic Driving Study (NDS) database to develop a model for detecting the likelihood of a driver's involvement in secondary tasks from distinctive attributes of driving behavior. Five performance attributes, namely speed, longitudinal acceleration, lateral acceleration, yaw rate, and throttle position were used to describe the driving behavior. A model was developed for each of three selected secondary tasks: calling, texting, and passenger interaction. The models were developed using a supervised feed-forward Artificial Neural Network (ANN) architecture to account for the effect of inherent nonlinearity in the relationships between driving behavior and secondary tasks. The results show that the developed ANN models were able to detect the drivers' involvement in calling, texting, and passenger interaction with an overall accuracy of 99.5%, 98.1%, and 99.8%, respectively. These results show that the selected driving performance attributes were effective in detecting the associated secondary tasks with driving behavior. The results are very promising and the developed models could potentially be applied in crash investigations to resolve legal disputes in traffic accidents.

Drivers׳ merging behavior data in highway work zones.

There have been growing research interests in finding a suitable work zone layout to improve work zone safety and traffic efficiency. This paper contains data supporting the research article entitled: Effects of work zone configurations and traffic density on performance variables and subjective workload (Shakouri et al., 2014 [1]). A full factorial experiment was conducted to compare the efficiency of two work zone configurations by using a driving simulator with two levels of work zone configuration, two levels of traffic density and three levels of sign placement as fixed factors. Seven female and 23 male participants completed the experiment. In this paper we present the data relating to demographic information of participants, driving simulator data and subjective workload evaluation of participants for each work zone.

Experimental modeling of the effect of hurricane wind forces on driving behavior and vehicle performance.

While traffic planning is important for developing a hurricane evacuation plan, vehicle performance on the roads during extreme weather conditions is critical to the success of the planning process. This novel study investigates the effect of gusty hurricane wind forces on the driving behavior and vehicle performance. The study explores how the parameters of a driving simulator could be modified to reproduce wind loadings experienced by three vehicle types (passenger car, ambulance, and bus) during gusty hurricane winds, through manipulation of appropriate software. Thirty participants were then tested on the modified driving simulator under five wind conditions (ranging from normal to hurricane category 4). The driving performance measures used were heading error and lateral displacement. The results showed that higher wind forces resulted in more varied and greater heading error and lateral displacement. The ambulance had the greatest heading errors and lateral displacements, which were attributed to its large lateral surface area and light weight. Two mathematical models were developed to estimate the heading error and lateral displacements for each of the vehicle types for a given change in lateral wind force. Through a questionnaire, participants felt the different characteristics while driving each vehicle type. The findings of this study demonstrate the valuable use of a driving simulator to model the behavior of different vehicle types and to develop mathematical models to estimate and quantify driving behavior and vehicle performance under hurricane wind conditions.

Post and during event effect of cell phone talking and texting on driving performance--a driving simulator study.

OBJECTIVE: A number of studies have been done in the field of driver distraction, specifically on the use of cell phone for either conversation or texting while driving. Researchers have focused on the driving performance of drivers when they were actually engaged in the task; that is, during the texting or phone conversation event. However, it is still unknown whether the impact of cell phone usages ceases immediately after the end of task. The primary objective of this article is to analyze the post-event effect of cell phone usage (texting and conversation) in order to verify whether the distracting effect lingers after the actual event has ceased. METHODS: This study utilizes a driving simulator study of 36 participants to test whether a significant decrease in driver performance occurs during cell phone usage and after usage. Surrogate measures used to represent lateral and longitudinal control of the vehicle were standard deviation (SD) of lane position and mean velocity, respectively. RESULTS: RESULTS suggest that there was no significant decrease in driver performance (both lateral and longitudinal control) during and after the cell phone conversation. For the texting event, there were significant decreases in driver performance in both the longitudinal and lateral control of the vehicle during the actual texting task. The diminished longitudinal control ceased immediately after the texting event but the diminished lateral control lingered for an average of 3.38 s. The number of text messages exchanged did not affect the magnitude or duration of the diminished lateral control. CONCLUSION: The result indicates that the distraction and subsequent elevated crash risk of texting while driving linger even after the texting event has ceased. This finding has safety and policy implications in reducing distracted driving.

Effects of work zone configurations and traffic density on performance variables and subjective workload.

This paper investigates the effect of changing work zone configurations and traffic density on performance variables and subjective workload. Data regarding travel time, average speed, maximum percent braking force and location of lane changes were collected by using a full size driving simulator. The NASA-TLX was used to measure self-reported workload ratings during the driving task. Conventional lane merge (CLM) and joint lane merge (JLM) were modeled in a driving simulator, and thirty participants (seven female and 23 male), navigated through the two configurations with two levels of traffic density. The mean maximum braking forces was 34% lower in the JLM configuration, and drivers going through the JLM configuration remained in the closed lane longer. However, no significant differences in speed were found between the two merge configurations. The analysis of self-reported workload ratings show that participants reported 15.3% lower total workload when driving through the JLM. In conclusion, the implemented changes in the JLM make it a more favorable merge configuration in both high and low traffic densities in terms of optimizing traffic flow by increasing the time and distance cars use both lanes, and in terms of improving safety due to lower braking forces and lower reported workload.

Safety evaluation of joint and conventional lane merge configurations for freeway work zones.

Inefficient operation of traffic in work zone areas not only leads to an increase in travel time delays, queue length, and fuel consumption but also increases the number of forced merges and roadway accidents. This study evaluated the safety performance of work zones with a conventional lane merge (CLM) configuration in Louisiana. Analysis of variance (ANOVA) was used to compare the crash rates for accidents involving fatalities, injuries, and property damage only (PDO) in each of the following 4 areas: (1) advance warning area, (2) transition area, (3) work area, and (4) termination area. The analysis showed that the advance warning area had higher fatality, injury, and PDO crash rates when compared to the transition area, work area, and termination area. This finding confirmed the need to make improvements in the advance warning area where merging maneuvers take place. Therefore, a new lane merge configuration, called joint lane merge (JLM), was proposed and its safety performance was examined and compared to the conventional lane merge configuration using a microscopic simulation model (VISSIM), which was calibrated with real-world data from an existing work zone on I-55 and used to simulate a total of 25 different scenarios with different levels of demand and traffic composition. Safety performance was evaluated using 2 surrogate measures: uncomfortable decelerations and speed variance. Statistical analysis was conducted to determine whether the differences in safety performance between both configurations were significant. The safety analysis indicated that JLM outperformed CLM in most cases with low to moderate flow rates and that the percentage of trucks did not have a significant impact on the safety performance of either configuration. Though the safety analysis did not clearly indicate which lane merge configuration is safer for the overall work zone area, it was able to identify the possibly associated safety changes within the work zone area under different traffic conditions.