Browsing by Author "Herbers, Eileen"
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- Development of an Infrastructure Based Data Acquisition System to Naturalistically Collect the Roadway EnvironmentSarkar, Abhijit; Papakis, Ioannis; Herbers, Eileen; Viray, Reginald (2023-12)Automatic traffic monitoring is becoming an important investment for transportation specialists, especially as the overall volume of traffic continues to increase, as do crashes at intersections. Infrastructure cameras can be a good source of information for automatic monitoring of traffic situations at intersections. However, efficient computer vision methods that can process the video data effectively are required for this endeavor. Intersection cameras often record video data that are of low quality and low frame rate, making them challenging to use. In this project, we have demonstrated how traffic cameras can be used to automatically track roadway agents, find their kinematic behavior, and devise a safety measurement strategy, leveraging recent advancements in computer vision and deep learning. In this process, we have specifically focused on the Virginia Beach area and used publicly available traffic data to demonstrate our results. We developed a full computer vision pipeline that trains a custom object detector specifically using traffic data. We also used an optical flow method and a graph neural network to improve the accuracy of object tracking. The tracked objects from the image frames were further used as a point source and mapped to their GPS locations. Finally, the speed of each object was calculated to understand the traffic dynamics and determine possible crash predictors. This information can be used to quickly alert traffic control operators to a specific intersection that likely needs their attention so that crashes can be mitigated.
- Exploratory Development of Algorithms for Determining Driver Attention StatusHerbers, Eileen; Miller, Marty; Neurauter, Luke; Walters, Jacob; Glaser, Daniel (SAGE, 2023-09)Objective: Varying driver distraction algorithms were developed using vehicle kinematics and driver gaze data obtained from a camera-based driver monitoring system (DMS). Background: Distracted driving characteristics can be difficult to accurately detect due to wide variation in driver behavior across driving environments. The growing availability of information about drivers and their involvement in the driving task increases the opportunity for accurately recognizing attention state. Method: A baseline for driver distraction levels was developed using a video feed of 24 separate drivers in varying naturalistic driving conditions. This initial assessment was used to develop four buffer-based algorithms that aimed to determine a driver's real-time attentiveness, via a variety of metrics and combinations thereof. Results: Of those tested, the optimal algorithm included ungrouped glance locations and speed. Notably, as an algorithm's performance of detecting very distracted drivers improved, its accuracy for correctly identifying attentive drivers decreased. Conclusion: At a minimum, drivers' gaze position and vehicle speed should be included when designing driver distraction algorithms to delineate between glance patterns observed at high and low speeds. Distraction algorithms should be designed with an understanding of their limitations, including instances in which they may fail to detect distracted drivers, or falsely notify attentive drivers. Application: This research adds to the body of knowledge related to driver distraction and contributes to available methods to potentially address and reduce occurrences. Machine learning algorithms can build on the data elements discussed to increase distraction detection accuracy using robust artificial intelligence.
- The Impact of Line-of-Sight and Connected Vehicle Technology on Mitigating and Preventing Crash and Near-Crash EventsHerbers, Eileen; Doerzaph, Zachary; Stowe, Loren (MDPI, 2024-01-12)Line-of-sight (LOS) sensors developed in newer vehicles have the potential to help avoid crash and near-crash scenarios with advanced driving-assistance systems; furthermore, connected vehicle technologies (CVT) also have a promising role in advancing vehicle safety. This study used crash and near-crash events from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) to reconstruct crash events so that the applicable benefit of sensors in LOS systems and CVT can be compared. The benefits of CVT over LOS systems include additional reaction time before a predicted crash, as well as a lower deceleration value needed to prevent a crash. This work acts as a baseline effort to determine the potential safety benefits of CVT-enabled systems over LOS sensors alone.
- Impacts of Connected Vehicle Technology on Automated Vehicle SafetyHerbers, Eileen; Stowe, Loren (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-05)Connected vehicle technologies have a promising role in advancing vehicle safety, but just how much of an impact can connected vehicles have on driver safety? This study uses crash and near-crash events from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) to reconstruct crash events so that the benefit of line-of-sight (LOS) systems and connected vehicle technologies (CVT) can be compared. The benefits of CVT over LOS systems includes additional reaction time before a predicted crash, as well as a lower deceleration value needed to prevent a crash. These values were then used to predict the probability of severe injury for any crashes that could occur. This work acts as a baseline effort to determine the potential safety benefits of CVT-enabled systems over line-of-sight technologies alone.
- Improving Methods to Measure Attentiveness through Driver MonitoringMiller, Marty; Herbers, Eileen; Walters, Jacob; Neurauter, Luke (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-07)Driver inattention poses a significant problem on today’s roadways, increasing risk for all road users. This report details our efforts in developing algorithms to detect driver inattention. A benchmark dataset was developed based on video review of driving events. Buffer-based algorithms were developed and compared using this benchmark dataset. The benchmark events were also used as a training dataset for machine learning models. Driver glance locations were important for determining driver attentiveness. In addition, vehicle speed was important for understanding the driving context, which was found to have a large impact on driver behavior.