Formulating a Strategy for Securing High-Speed Rail in the United States, Research Report 12-03

This report presents an analysis of information relating to attacks, attempted attacks, and plots against high-speed rail (HSR) systems. It draws upon empirical data from MTI’s Database of Terrorist and Serious Criminal Attacks Against Public Surface Transportation and from reviews of selected HSR systems, including onsite observations. The report also examines the history of safety accidents and other HSR incidents that resulted in fatalities, injuries, or extensive asset damage to examine the inherent vulnerabilities (and strengths) of HSR systems and how these might affect the consequences of terrorist attacks. The study is divided into three parts: (1) an examination of security principles and measures; (2) an empirical examination of 33 attacks against HSR targets and a comparison of attacks against HSR targets with those against non-HSR targets; and (3) an examination of 73 safety incidents on 12 HRS systems. The purpose of this study is to develop an overall strategy for HSR security and to identify measures that could be applied to HSR systems currently under development in the United States. It is hoped that the report will provide useful guidance to both governmental authorities and transportation operators of current and future HSR systems

Development of rear-end collision avoidance in automobiles

The goal of this work is to develop a Rear-End Collision Avoidance System for automobiles. In order to develop the Rear-end Collision Avoidance System, it is stated that the most important difference from the old practice is the fact that new design approach attempts to completely avoid collision instead of minimizing the damage by over-designing cars. Rear-end collisions are the third highest cause of multiple vehicle fatalities in the U.S. Their cause seems to be a result of poor driver awareness and communication. For example, car brake lights illuminate exactly the same whether the car is slowing, stopping or the driver is simply resting his foot on the pedal. In the development of Rear-End Collision Avoidance System (RECAS), a thorough review of hardware, software, driver/human factors, and current rear-end collision avoidance systems are included. Key sensor technologies are identified and reviewed in an attempt to ease the design effort. The characteristics and capabilities of alternative and emerging sensor technologies are also described and their performance compared. In designing a RECAS the first component is to monitor the distance and speed of the car ahead. If an unsafe condition is detected a warning is issued and the vehicle is decelerated (if necessary). The second component in the design effort utilizes the illumination of independent segments of brake lights corresponding to the stopping condition of the car. This communicates the stopping intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions. In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor

Analysis of Haul Truck- Related Fatalities and Injuries in Surface Coal Mining in West Virginia

Trucks are the primary means of haulage in surface coal, metal, and nonmetal mining operations. The number of fatal accidents involving trucks is higher when compared to all other mining equipment. The Mine Safety and Health Administration (MSHA) reports that 137 fatalities were haul truck- related in the United States between 1995 and 2011. A total of 12 truck-related accidents, including 13 fatalities, were recorded in surface coal mining operations in West Virginia (WV) during this period. The objectives of this research were to (i) analyze the root causes of these accidents, and (ii) develop effective intervention strategies to eliminate these fatalities. The Fault Tree Analysis (FTA) technique was used to systematically analyze truck related fatalities. Data on truck-related injury accidents in West Virginia surface coal mines during 2012 and 2013 were also analyzed in this study. Results of the study indicate that inadequate or improper pre-operational check and poor maintenance of trucks were the two most common root causes of these accidents. A total of eight accidents occurred on haul roads, while 10 accidents occurred while the trucks were moving forward. The two most violated provisions of Code of Federal Regulations were 30 CFR§77.404 - Machinery and equipment; operation and maintenance (six times), and 30 CFR§77.1606 - Loading and haulage equipment; inspection and maintenance (five times).;A total of 223 reported injuries were recorded at West Virginia surface coal mines. With the exception of two missing data, a total of 178 accidents were equipment-related and 43 accidents occurred without equipment being involved. The equipment categories accounting for the most number of injuries were: truck (57 times) and bulldozer/dozer/crawler tractor (43 times). The majority of the truck-related injuries occurred within the worker\u27s first five years at the mine and within the first five years at their current job title. Workers between ages 25 and 39 had the greatest percentage of injuries. Most injuries were recorded during Section I (6:00 a.m. - 2:00 p.m.), and the fall season has the greatest number of truck-related injuries of all four seasons. Regarding the nature of injury, sprains and strains made up about 32%, topping all other types of injuries. The most commonly injured body part in truck-related injuries was the Multiple parts. .;A two-pronged approach to accident prevention was used: one that is fundamental and traditional (safety regulations, training and education, and engineering of the work environment); and one that is innovative and creative (e.g., applying technological advances to better control and eliminate the root causes of accidents). Suggestions for improving current training and education system were proposed, and recommendations were provided on improving the safety of mine working conditions, specifically safety conditions on haul roads, dump sites, and loading areas. Currently available technologies that can help prevent haul truck-related fatal accidents were also discussed. The results of this research may be used by mine personnel to help create safer working conditions and decrease truck-related fatalities and injuries in surface coal mining

Development and evaluation of a smartphone-based system for inspection of road maintenance work

Abstract. In the road construction industry, doing work inspection is a laborious and resource-consuming job because of the distributed work site. Contractors in Finland require to capture photos of every road fix they have done as proof of their work. It is well-established that with the help of smartphone technology, these kinds of manual work can be reduced. This thesis aims to develop and evaluate a smartphone-based system to capture video evidence of task completion. The system, designed and developed in this thesis, consists of an Android application named ’Road Recorder’ and a web tool for managing the content collected by Road Recorder. While mounted to a vehicle’s dashboard used in construction work, the Road Recorder can record the videos of road surface and geo-location information and some other metadata and send them to a remote server that is inspected using the web tool. Users of different backgrounds were given the system to accomplish some tasks and were observed closely. The users were interviewed at the end, and responses were analyzed to find the usability of the applications. The results indicate the high usability of the Road Recorder application and reveal possible improvements for the Road Recorder management web application. Overall, Road Recorder is a great step towards the automation of such construction work inspection. Though there were some limitations in the evaluation process, it demonstrates that Road Recorder is easy to use and can be a useful tool in the industry

DEVELOPMENT OF A NOVEL VEHICLE GUIDANCE SYSTEM: VEHICLE RISK MITIGATION AND CONTROL

Over a half of fatal vehicular crashes occur due to vehicles leaving their designated travel lane and entering other lanes or leaving the roadway. Lane departure accidents also result in billions of dollars in cost to society. Recent vehicle technology research into driver assistance and vehicle autonomy has developed to assume various driving tasks. However, these systems are do not work for all roads and travel conditions. The purpose of this research study was to begin the development a novel vehicle guidance approach, specifically studying how the vehicle interacts with the system to detect departures and control the vehicle A literature review was conducted, covering topics such as vehicle sensors, control methods, environment recognition, driver assistance methods, vehicle autonomy methods, communication, positioning, and regulations. Researchers identified environment independence, recognition accuracy, computational load, and industry collaboration as areas of need in intelligent transportation. A novel method of vehicle guidance was conceptualized known as the MwRSF Smart Barrier. The vision of this method is to send verified road path data, based AASHTO design and vehicle dynamic aspects, to guide the vehicle. To further development research was done to determine various aspects of vehicle dynamics and trajectory trends can be used to predict departures and control the vehicle. Tire-to-road friction capacity and roll stability were identified as traits that can be prevented with future road path knowledge. Road departure characteristics were mathematically developed. It was shown that lateral departure, orientation error, and curvature error are parametrically linked, and discussion was given for these metrics as the basis for of departure prediction. A three parallel PID controller for modulating vehicle steering inputs to a virtual vehicle to remain on the path was developed. The controller was informed by a matrix of XY road coordinates, road curvature and future road curvature and was able to keep the simulated vehicle to within 1 in of the centerline target path. Recommendations were made for the creation of warning modules, threshold levels, improvements to be applied to vehicle controller, and ultimately full-scale testing. Advisor: Cody S. Stoll

Advances in Automated Driving Systems

Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic

Modeling driver distraction mechanism and its safety impact in automated vehicle environment.

Automated Vehicle (AV) technology expects to enhance driving safety by eliminating human errors. However, driver distraction still exists under automated driving. The Society of Automotive Engineers (SAE) has defined six levels of driving automation from Level 0~5. Until achieving Level 5, human drivers are still needed. Therefore, the Human-Vehicle Interaction (HVI) necessarily diverts a driver’s attention away from driving. Existing research mainly focused on quantifying distraction in human-operated vehicles rather than in the AV environment. It causes a lack of knowledge on how AV distraction can be detected, quantified, and understood. Moreover, existing research in exploring AV distraction has mainly pre-defined distraction as a binary outcome and investigated the patterns that contribute to distraction from multiple perspectives. However, the magnitude of AV distraction is not accurately quantified. Moreover, past studies in quantifying distraction have mainly used wearable sensors’ data. In reality, it is not realistic for drivers to wear these sensors whenever they drive. Hence, a research motivation is to develop a surrogate model that can replace the wearable device-based data to predict AV distraction. From the safety perspective, there lacks a comprehensive understanding of how AV distraction impacts safety. Furthermore, a solution is needed for safely offsetting the impact of distracted driving. In this context, this research aims to (1) improve the existing methods in quantifying Human-Vehicle Interaction-induced (HVI-induced) driver distraction under automated driving; (2) develop a surrogate driver distraction prediction model without using wearable sensor data; (3) quantitatively reveal the dynamic nature of safety benefits and collision hazards of HVI-induced visual and cognitive distractions under automated driving by mathematically formulating the interrelationships among contributing factors; and (4) propose a conceptual prototype of an AI-driven, Ultra-advanced Collision Avoidance System (AUCAS-L3) targeting HVI-induced driver distraction under automated driving without eye-tracking and video-recording. Fixation and pupil dilation data from the eye tracking device are used to model driver distraction, focusing on visual and cognitive distraction, respectively. In order to validate the proposed methods for measuring and modeling driver distraction, a data collection was conducted by inviting drivers to try out automated driving under Level 3 automation on a simulator. Each driver went through a jaywalker scenario twice, receiving a takeover request under two types of HVI, namely “visual only” and “visual and audible”. Each driver was required to wear an eye-tracker so that the fixation and pupil dilation data could be collected when driving, along with driving performance data being recorded by the simulator. In addition, drivers’ demographical information was collected by a pre-experiment survey. As a result, the magnitude of visual and cognitive distraction was quantified, exploring the dynamic changes over time. Drivers are more concentrated and maintain a higher level of takeover readiness under the “visual and audible” warning, compared to “visual only” warning. The change of visual distraction was mathematically formulated as a function of time. In addition, the change of visual distraction magnitude over time is explained from the driving psychology perspective. Moreover, the visual distraction was also measured by direction in this research, and hotspots of visual distraction were identified with regard to driving safety. When discussing the cognitive distraction magnitude, the driver’s age was identified as a contributing factor. HVI warning type contributes to the significant difference in cognitive distraction acceleration rate. After drivers reach the maximum visual distraction, cognitive distraction tends to increase continuously. Also, this research contributes to quantitatively revealing how visual and cognitive distraction impacts the collision hazards, respectively. Moreover, this research contributes to the literature by developing deep learning-based models in predicting a driver’s visual and cognitive distraction intensity, focusing on demographics, HVI warning types, and driving performance. As a solution to safety issues caused by driver distraction, the AUCAS-L3 has been proposed. The AUCAS-L3 is validated with high accuracies in predicting (a) whether a driver is distracted and does not perform takeover actions and (b) whether crashes happen or not if taken over. After predicting the presence of driver distraction or a crash, AUCAS-L3 automatically applies the brake pedal for drivers as effective and efficient protection to driver distraction under automated driving. And finally, a conceptual prototype in predicting AV distraction and traffic conflict was proposed, which can predict the collision hazards in advance of 0.82 seconds on average