Connected Simulation for Work Zone Safety Application

Every year, over 60,000 work zone crashes are reported in the United States (FHWA 2016). Such work zone crashes have resulted in over 4,400 fatal and 200,000 non-fatal injuries in the last 5 years (FHWA 2016, BLS 2014). Apart from the physical and emotional trauma, the annual cost of these injuries exceeds $4 million-representing significant wasted resources. To improve work zone safety, this research developed a system architecture for unveiling high-risk behavioral patterns among highway workers, equipment operators, and drivers within dynamic highway work zones. This research implemented the use of a connected virtual environment, which is an immersive hyper-realistic and virtual environment where multiple agents (e.g. workers, drivers, and equipment handlers) control independent simulators but experience an interactive and shared experience. For this project, the team conducted an in-depth analysis of accident investigation, simulated accident scenarios, and tested diverse interventions to prevent high-risk behavior. Overall, the research improved understanding of behavioral patterns that lead to injuries and fatalities of highway workers in order to better protect them in high-risk work environments. As part of making transportation smarter, this project contributes to smart behavioral safety analysis

System-of-Systems Integration for Civil Infrastructures Resiliency Toward MultiHazard Events

Civil infrastructure systems—facilities that supply principal services, such as electricity, water, transportation, etc., to a community—are the backbone of modern society. These systems are frequently subject to multi-hazard events, such as earthquakes. The poor resiliency of these infrastructures results in many human casualties and significant economic losses every year. An outline of a holistic view that considers how different civil infrastructure systems operate independently and how they interact and communicate with each other is required to have a resilient infrastructure system. More specifically a systems engineering approach is required to enable infrastructure to remain resilient in the case of extreme events, including natural disasters. To address these challenges, this research builds on the proposal that the infrastructure systems be equipped with state-of-the-art sensor networks that continuously record the condition and performance of the infrastructure. The sensor data from each infrastructure are then transferred to a data analysis system component that employs artificial intelligence techniques to constantly analyze the infrastructure’s resiliency and energy efficiency performance. This research models the resilient infrastructure problem as a System of Systems (SoS) comprised of the abovementioned components. It explores system integration and operability challenges and proposes solutions to meet the requirements of the SoS. An integration ontology, as well as a data-centric architecture, is developed to enable infrastructure resiliency toward multi-hazard events. The Federal Emergency Management Agency (FEMA), and infrastructure managers, such as Departments of Transportation (DOTs) and the Federal Highway Administration (FHWA), can learn from and integrate these solutions to make civil infrastructure systems more resilient for all

Image-based recognition, 3D localization, and retro-reflectivity evaluation of high-quantity low-cost roadway assets for enhanced condition assessment

Systematic condition assessment of high-quantity low-cost roadway assets such as traffic signs, guardrails, and pavement markings requires frequent reporting on location and up-to-date status of these assets. Today, most Departments of Transportation (DOTs) in the US collect data using camera-mounted vehicles to filter, annotate, organize, and present the data necessary for these assessments. However, the cost and complexity of the collection, analysis, and reporting as-is conditions result in sparse and infrequent monitoring. Thus, some of the gains in efficiency are consumed by monitoring costs. This dissertation proposes to improve frequency, detail, and applicability of image-based condition assessment via automating detection, classification, and 3D localization of multiple types of high-quantity low-cost roadway assets using both images collected by the DOTs and online databases such Google Street View Images. To address the new requirements of US Federal Highway Administration (FHWA), a new method is also developed that simulates nighttime visibility of traffic signs from images taken during daytime and measures their retro-reflectivity condition. To initiate detection and classification of high-quantity low-cost roadway assets from street-level images, a number of algorithms are proposed that automatically segment and localize high-level asset categories in 3D. The first set of algorithms focus on the task of detecting and segmenting assets at high-level categories. More specifically, a method based on Semantic Texton Forest classifiers, segments each geo-registered 2D video frame at the pixel-level based on shape, texture, and color. A Structure from Motion (SfM) procedure reconstructs the road and its assets in 3D. Next, a voting scheme assigns the most observed asset category to each point in 3D. The experimental results from application of this method are promising, nevertheless because this method relies on using supervised ground-truth pixel labels for training purposes, scaling it to various types of assets is challenging. To address this issue, a non-parametric image parsing method is proposed that leverages lazy learning scheme for segmentation and recognition of roadway assets. The semi-supervised technique used in the proposed method does not need training and provides ground truth data in a more efficient manner. It is easily scalable to thousands of video frames captured during data collection. Once the high-level asset categories are detected, specific techniques needs to be exploited to detect and classify the assets at a higher level of granularity. To this end, performance of three computer vision algorithms are evaluated for classification of traffic signs in presence of cluttered backgrounds and static and dynamic occlusions. Without making any prior assumptions about the location of traffic signs in 2D, the best performing method uses histograms of oriented gradients and color together with multiple one-vs-all Support Vector Machines, and classifies these assets into warning, regulatory, stop, and yield sign categories. To minimize the reliance on visual data collected by the DOTs and improve frequency and applicability of condition assessment, a new end-to-end procedure is presented that applies the above algorithms and creates comprehensive inventory of traffic signs using Google Street View images. By processing images extracted using Google Street View API and discriminative classification scores from all images that see a sign, the most probable 3D location of each traffic sign is derived and is shown on the Google Earth using a dynamic heat map. A data card containing information about location, type, and condition of each detected traffic sign is also created. Finally, a computer vision-based algorithm is proposed that measures retro-reflectivity of traffic signs during daytime using a vehicle mounted device. The algorithm simulates nighttime visibility of traffic signs from images taken during daytime and measures their retro-reflectivity. The technique is faster, cheaper, and safer compared to the state-of-the-art as it neither requires nighttime operation nor requires manual sign inspection. It also satisfies measurement guidelines set forth by FHWA both in terms of granularity and accuracy. To validate the techniques, new detailed video datasets and their ground-truth were generated from 2.2-mile smart road research facility and two interstate highways in the US. The comprehensive dataset contains over 11,000 annotated U.S. traffic sign images and exhibits large variations in sign pose, scale, background, illumination, and occlusion conditions. The performance of all algorithms were examined using these datasets. For retro-reflectivity measurement of traffic signs, experiments were conducted at different times of day and for different distances. Results were compared with a method recommended by ASTM standards. The experimental results show promise in scalability of these methods to reduce the time and effort required for developing road inventories, especially for those assets such as guardrails and traffic lights that are not typically considered in 2D asset recognition methods and also multiple categories of traffic signs. The applicability of Google Street View Images for inventory management purposes and also the technique for retro-reflectivity measurement during daytime demonstrate strong potential in lowering inspection costs and improving safety in practical applications

How do Environmental Factors Affect Drivers’ Gaze and Head Movements?

Studies have shown that environmental factors affect driving behaviors. For instance, weather conditions and the presence of a passenger have been shown to significantly affect the speed of the driver. As one of the important measures of driving behavior is the gaze and head movements of the driver, such metrics can be potentially used towards understanding the effects of environmental factors on the driver’s behavior in real-time. In this study, using a naturalistic study platform, videos have been collected from six participants for more than four weeks of a fully naturalistic driving scenario. The videos of both the participants’ faces and roads have been cleaned and manually categorized depending on weather, road type, and passenger conditions. Facial videos have been analyzed using OpenFace to retrieve the gaze direction and head movements of the driver. Results, overall, suggest that the gaze direction and head movements of the driver are affected by a combination of environmental factors and individual differences. Specifically, results depict the distracting effect of the passenger on some individuals. In addition, it shows that highways and city streets are the cause for maximum distraction on the driver’s gaze

A Multimodal Approach for Monitoring Driving Behavior and Emotions

Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways

Developing a Computer Vision-Based Decision Support System for Intersection Safety Monitoring and Assessment of Vulnerable Road Users

Vision-based trajectory analysis of road users enables identification of near-crash situations and proactive safety monitoring. The two most widely used sur-rogate safety measures (SSMs), time-to-collision (TTC) and post-encroachment time (PET)—and a recent variant form of TTC, relative time-to-collision (RTTC)—were investigated using real-world video data collected at ten signalized intersections in the city of San Diego, California. The performance of these SSMs was compared for the purpose of evaluating pedestrian and bicyclist safety. Prediction of potential trajectory intersection points was performed to calculate TTC for every interacting object, and the average of TTC for every two objects in critical situations was calculated. PET values were estimated by observing potential intersection points, and frequencies of events were estimated in three critical levels. Although RTTC provided useful information regarding the relative distance between objects in time, it was found that in certain conditions where objects are far from each other, the interaction between the objects was incorrectly flagged as critical based on a small RTTC. Comparison of PET, TTC, and RTTC for different critical classes also showed that several interactions were identified as critical using one SSM but not critical using a different SSM. These findings suggest that safety evaluations should not solely rely on a single SSM, and instead a combination of different SSMs should be considered to ensure the reliability of evaluations. Video data analysis was conducted to develop object detection and tracking models for automatic identification of vehicles, bicycles, and pedestrians. Outcomes of machine vision models were employed along with SSMs to build a decision support system for safety assessment of vulnerable road users at signalized intersections. Promising results from the decision support system showed that automated safety evaluations can be performed to proactively identify critical events. It also showed challenges as well as future directions to enhance the performance of the system

System-of-Systems Integration for Civil Infrastructures Resiliency toward Multi-Hazard Events

ZSB12017-SJAUXCivil infrastructure systems\u2014facilities that supply principal services, such as electricity, water, transportation, etc., to a community\u2014are the backbone of modern society. These systems are frequently subject to multi-hazard events, such as earthquakes. The poor resiliency of these infrastructures results in many human casualties and significant economic losses every year. An outline of a holistic view that considers how different civil infrastructure systems operate independently and how they interact and communicate with each other is required to have a resilient infrastructure system. More specifically a systems engineering approach is required to enable infrastructure to remain resilient in the case of extreme events, including natural disasters. To address these challenges, this research builds on the proposal that the infrastructure systems be equipped with state-of-the-art sensor networks that continuously record the condition and performance of the infrastructure. The sensor data from each infrastructure are then transferred to a data analysis system component that employs artificial intelligence techniques to constantly analyze the infrastructure\u2019s resiliency and energy efficiency performance. This research models the resilient infrastructure problem as a System of Systems (SoS) comprised of the abovementioned components. It explores system integration and operability challenges and proposes solutions to meet the requirements of the SoS. An integration ontology, as well as a data-centric architecture, is developed to enable infrastructure resiliency toward multi-hazard events. The Federal Emergency Management Agency (FEMA), and infrastructure managers, such as Departments of Transportation (DOTs) and the Federal Highway Administration (FHWA), can learn from and integrate these solutions to make civil infrastructure systems more resilient for all

Effect of Sulfur Mustard Toxicity on FLT3-ITD Gene Mutation in Sulfur Mustard Veterans

Background: Sulfur mustard (SM) is a chemical blistering warfare that affects different organs especially hematopoietic system. Prevalence of acute myeloblastic and lymphoblastic leukemia is increased by sulfur mustard exposure. FLT3-ITD mutation can be effective on leukemogenesis. Therefore, the aim of this study was to verify the frequency of FLT3-ITD mutation in the patients who exposed to SM. Methods: This study was implemented on 42 people poisoned by SM during Iraq-Iran war about three decades ago and is now resident in Mashhad, Iran. The control group included 30 healthy males that are relatives of the patients with first-degree. After DNA extraction, PCR was performed for FLT3-ITD analysis. Results: By analysis of PCR products, no FLT3-ITD mutation was detected in the patient or control groups. There was no significant difference in hematological factors between the two groups. Conclusion: Other mechanisms can lead to leukemia in SM exposed persons. Elapsed time after exposure to sulfur mustard can be effective on leukemogenesis, then future more study may be beneficial for early diagnosis of leukemia in SM exposed veterans

Vector Maps Mobile Application for Sustainable Eco-Driving Transportation Route Selection

The decisions managing all modes of transportation are currently based on the traffic rate and travel time. However, other factors such as Green House Gas (GHG) emissions, the sustainability index, fuel consumption, and travel costs are not considered. Therefore, more comprehensive methods need to be implemented to improve transportation systems and support users’ decision making in their daily commute. This paper addresses current challenges by utilizing data analytics derived from our proposed mobile application. The proposed application quantifies various factors of each transportation mode including but not limited to the cost, trip duration, fuel consumption, and Carbon Dioxide (CO2) emissions. All calculated travel costs are based on the real-time gas prices and toll fees. The users are also able to navigate to their destination and update the total travel costs in real-time. The emissions data per trip basis are aggregated to provide analytics of emissions usage. The traffic data is collected for the Southern California region and the effectiveness of the application is evaluated by twenty participants from California State University, Long Beach. The results demonstrate the application’s impacts on users’ decision-making and the propriety of the factors used in route selection. The proposed application can foster urban planning and operations vis-à-vis daily commutes, and as a result improve the citizens’ quality of life in various aspects