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This project involved the development of a fault diagnostic system for Safetruck, an intelligent vehicle prototype. The fault diagnostic system continuously monitors the health of vehicle sensors, detects a failure when it happens, and identifies the source of the failure. The fault diagnostic system monitors several key components: the Global Positioning System, lateral accelerometer, and yaw-rate gyroscope, which constitute the set of lateral dynamic sensors, as well as the forward-looking radar that measures distance relative velocity, and azimuth angle to other vehicles and objects on the highway. To design the project's lateral fault diagnostic system, researchers exploited the model-based dynamic relationships that exist between the three lateral sensors. They verified the system's performance through extensive experiments on the Safetruck. This project also explored a number of new approaches to creating a reliable fault detection system for radar. Monitoring the radar's health poses a special challenge because the radar measures the distance to another independent vehicle on the highway. In the absence of inter-vehicle communications, the fault diagnostic system has no way of knowing the other vehicle's motion, which means that model-based approaches cannot be used. Experimental results indicate that an inexpensive redundant sensor combined with a specially designed nonlinear filter would provide the most reliable method for radar health monitoring.

This report documents the evaluation efforts undertaken by the Minnesota Team. To complement the work undertaken by the independent government evaluator, Battelle, the Minnesota evaluation team focused on two specific areas of the evaluation: human factors and benefit-cost analyses. Human factors issues include driver acceptance, reduction in driver fatigue, the effectiveness of the driver interface, and the measurable changes in driver performance. The Driver Assistive System (DAS), which is under evaluation for the US DOT Specialty Vehicle Generation Zero Field Operational Test, is designed to provide a driver a means to maintain desired lane position and avoid collisions with obstacles during periods of very low visibility. This program is motivated by the fact that specialty vehicles often must operate under inclement weather conditions and associated low visibility situations. The DAS improves safety for the specialty vehicle operator by providing the necessary cues for lane keeping and collision avoidance normally unavailable during poor visibility conditions. The DAS may also improve safety conditions for the general public by facilitating all-weather emergency services, and in the case of snowplows, opening roads and keeping them passable in heavy weather for other emergency vehicles and the general motoring public.

The objectives of this assessment are: to estimate the potential benefits of the Driver Assist System (DAS) for winter maintenance activities; to assess and describe the potential market for the DAS technologies as well as the approximate price point at which the system would be commercially viable, and; to determine where, geographically, DAS technology would be most cost-effective. The findings presented in this report are based on information that was gathered through an extensive literature review and a series of interviews conducted with state and county maintenance engineers and supervisors, equipment vendors, system integrators, equipment procurement personnel, and individuals involved in various aspects of risk management for transportation agencies. The expected benefits of DAS on winter maintenance vehicles include the reduction in travel times, less disruption to routine travel behavior and improved safety for the traveling public during and immediately following winter weather events. Winter weather events have a substantial impact on traveler safety, economic activity, and transportation maintenance costs. The functional objective of the DAS is to provide snowplow operators a means to operate snowplows during periods of low visibility. The study assesses the issue of visibility from the perspective of both snowplow operators and maintenance engineers.

This study aimed to determine the usefulness of the Driver Assistive System (DAS) in the context of plowing roads during low-visibility conditions. Driving performance, driver workload, and system performance were to be compared in a field operational test (FOT). Geographical location of the driver's route proved to play a large part in the desirability and perceived reliability of the system, as rural drivers preferred the system due to the lack of lighting and visual guidance while driving in low-visibility conditions. Most drivers did not have problems remembering how to use the DAS, and that the system made them feel safer and more in control while driving. The haptic seat was praised for giving warnings while letting them keep their eyes on the road or performing other in-cab tasks and their ideal configuration would be to use the haptic seat and/or the HUD. Due to an uncharacteristically mild winter weather conditions, it was decided that the FOT would not provide enough experience using the DAS during low-visibility conditions to make reasonable conclusions on driving performance. Therefore, it was necessary to use an additional experimental design with a track test, which is discussed in a supplemental document (Rakauskas et al., 2003).

This track test supplements an attempted field operational test (Rakauskas et al., 2003) which did not provide enough experience using the Driver Assistive System (DAS) during low-visibility conditions to make reasonable conclusions on driving performance. This study aimed to determine the usefulness of the DAS in the context of simulated low-visibility conditions. Drivers drove in clear, low-visibility, and DAS-assisted low-visibility conditions. Driving performance measures were taken while driving and drivers were asked workload, trust, and subjective response questions after each condition and post-experiment. The DAS enabled drivers to maintain consistent lane position and to make fewer steering corrections than while driving the low-visibility condition. Using the DAS during low-visibility conditions did not change speed performance and aided the driver by providing additional information about the road. More mental effort was reported while assisted by the DAS than while driving unassisted in the low-visibility condition. This was expected since drivers were presented with and were expected to mentally process more information while assisted. Many of the trends found were consistent with our previous thoughts on how the DAS would perform. However, due to the small number of drivers tested in the FOT and track testing studies there was low power for our statistical analyses. We encourage further research with the DAS on larger numbers of drivers or in a more powerful study design. Some changes are also recommended for future versions, such as providing a warning prior to loss of GPS fix. Project study to assess the usefulness of the Driver Assistive System (DAS) in the context of driving snowplows in low-visibility conditions on a test track. The system was found to be useful; several design improvements to the system are suggested to maximize its effectiveness.

Connected and Automated Vehicles (CAVs) are expected to affect the foundations of transportation operations and roadway maintenance as they become more prevalent on the roadways. This report is an effort to address this complex subject for the various owners; agencies and stakeholders involved in traffic operations. It discusses the connected vehicle ecosystem and its background; potential CAV applications; types of communication and hardware required for CAV systems; and recommendations to local road owners. The report also includes a survey sent to local road owners to assess the current readiness of the transportation system for CAVs. Although it is too early to give specific recommendations; general guidance is provided for road owners to begin preparing for the future of CAVs.

Basic Safety Message (BSM) containing data about the vehicle's position, speed, and acceleration. Roadside receivers, RSUs, can capture BSM broadcasts and translate them into information about traffic conditions. If every vehicle is equipped with awareness, BSMs can be combined to calculate traffic flows, speeds, and densities. These three key parameters will be post-processed to obtain queue lengths and travel time estimates. The project team proposed a traffic state estimation algorithm using BSMs based on the Kalman filter technique. The algorithm's performance was tested with BSMs generated from several arterial in a microscopic simulation model and BSMs generated with radar data collected on freeway sections. Then the project team developed a traffic monitoring system to apply the algorithm to a large-scale network with different types of roads. In the system, computers could remotely access the online server to acquire BSMs and estimate traffic states in real-time.

This Technical Summary pertains to report 2021-08, Generating Traffic Information from Connected Vehicle V2V Basic Safety Messages.

This Technical Summary pertains to report 2019-35, How Locals Need to Prepare for the Future of V2V/V2I Connected Vehicles.