Gorjestani, Alec

Heavy or blowing snow often causes poor visibility for snowplows. This report presents the results of a one-year preliminary study to evaluate the performance of an off-the-shelf radar unit for improved detection of objects under snow and blizzard conditions. Researchers developed a geometrical computer model of radar range and closure rate measurement to provide a baseline for comparison with experimental results. They varied parameters such as radar orientation, location, and differential vehicle speed to determine their effect on radar performance. The radar's accuracy improves as the speed differential between vehicles increases, according to the research findings. Furthermore, slight deviations in orientation and location do not seem to greatly influence the radar's ability to detect other vehicles. The radar also was tested under falling snow conditions. The radar effectively detected target vehicles under 'light' and 'moderate' snow conditions with visibility down to less than one half mile. However, the very small number of snow events in the winter of 1997-98 limits the ability to make conclusions about the radar's performance under such conditions. Since the study began, commercially available radar technology has improved significantly, and researchers recommend testing the improved radar units in the future.

Motor vehicle crashes are the leading cause of teen fatalities. A Teen Driver Support System (TDSS) was developed by the ITS Institute that can allow parents to accurately monitor their teen's driving behavior in relation to known risk factors and Graduated Driver Licensing (GDL) provisions. The TDSS, based on a teen's smart phone, provides real-time, contextual in-vehicle feedback to the teen about his or her driving behavior and helps parents monitor certain known risk factors. The system does not allow incoming or outgoing phone calls (except 911) or texting while driving. Feedback to the teen driver includes visual and auditory warnings about speeding, excessive maneuvers (e.g., hard braking, cornering), and stop sign violations. The TDSS prototype also monitors seat belt use and detects the presence of passengers (e.g., based on GDL provisions), two known factors that increase the risk of fatalities among teen drivers. The TDSS can also be programmed to monitor driving during the GDL curfew or a curfew set by parents. A usability review of the prototype TDSS using 30 parent-teen dyads from Washington Country, MN, found that teens and parents held favorable opinions about most of the TDSS functions. Teens and parents both felt that use of the system early in licensure may result in the adoption of safer driving habits even after the system is removed from the vehicle. Several recommendations to improve the system's usability are made based on the results.

Report #3 in the series: Developing Intersection Decision Support Solutions. In rural Minnesota, approximately one-third of all crashes occur at intersections. Analysis of crash statistics and reports of crashes at rural expressway through-stop intersections shows that, for drivers who stop before entering the intersection, the majority of crashes involve an error in selecting a safe gap in traffic. The Intersection Decision Support system, developed at the University of Minnesota, is intended to reduce the number of driver errors by providing better information about oncoming traffic to drivers stopped at intersections. This report deals primarily with the surveillance technology which serves as the foundation upon which the IDS system will be built. Three components of the surveillance system are described in detail in the body of the report: 1) a Mainline Sensor subsystem; 2) a Minor Road Sensor subsystem; 3) a Median Sensor subsystem. These subsystems include radar units, laser-scanning sensors, and infrared cameras, integrated with a vehicle tracking and classification unit that estimates the states of all vehicles approaching the intersection. The design, installation, performance, and reliability of each of these three subsystems are documented in the report. The report concludes with an analysis of driver gap acceptance behavior at an instrumented intersection. Gap selection is examined as a function of time of day, traffic levels, weather conditions, maneuver, and other parameters. Log-normal distributions describe gaps acceptance behavior at rural, unsignalized expressway intersections.

Deployment of any system is driven by market demand and system cost. Initial deployment of the Intelligent Vehicle Lab Snowplow Driver Assistive System (DAS) was limited to a 45 mile section of Minnesota Trunk Highway 7 west of I-494 and east of Hutchinson MN. To better gage demand and functionality, St. Louis and Polk Counties in Minnesota operationally tested the system during the winter of 2003-2004; Polk County also tested during the winter of 2004-2005. Operational benefits were found to be drastically different in the two counties. Low visibility was not an issue with the St. Louis County snowplow routes, so the system offered few benefits. In contrast the topology of Polk county is flat, with almost no trees. High winds combined with few visual cues create significant low visibility conditions. Polk County was pleased with their original system, and obtained a second system and tested it operationally during the 2004-2005 winter. The experience of these two counties is documented in this volume, Volume One. A key component of the DAS is a high accuracy digital map. With the exception of the mapping process, the present cost of the DAS is well documented. Volume Two describes a system designed to collect and process geospatial data to be used by driver assistive system, and the costs and time associated with collecting map data, and creating a map from that data. With cost data complete, counties can determine whether to acquire these systems.

Gang plowing is one method used by the Minnesota Department of Transportation (Mn/DOT) to increase the productivity of snowplow operations. However, these gains in productivity often come at the expense of increased driver stress. These higher stress levels are the result of the low visibility caused by localized snow clouds created by the lead snowplow, and by anxious drivers trying to pass between the moving plows. To improve the gang plowing process, a DGPS-based gang plowing system has been developed. This system uses advanced technology to allow a trailing snowplow to automatically follow a lead snowplow at an operator-specified lateral and longitudinal offset. The system is designed to improve both safety and productivity. This report covers three areas. First, to improve driver visibility, an implementation of the virtual mirror to the left side of the trailing plow is described. Second, the lateral and longitudinal performance of a two-vehicle gang on Minnesota Trunk Highway 101 is described. Third, a system architecture for gangs of more than two vehicles is proposed, and its potential performance is documented through simulation. Finally, recommendations for further research and other potential applications are provided.

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.

A comprehensive driver assistive system which utilizes dual frequency, carrier phase real time kinematic (RTK) differential global positioning system (DGPS), high accuracy digital geospatial databases, advanced automotive radar, and a driver interface with visual, haptic, and audible components has been used to assist specialty vehicle operators perform their tasks under these low visibility conditions. The system is able to provide a driver with high fidelity representations of the local geospatial landscape through a custom designed Head Up Display (HUD). Lane boundaries, turn lanes, intersections, mailboxes, and other elements of the geospatial landscape, including those sensed by automotive radar, are projected onto the HUD in the proper perspective. This allows a driver to safely guide his or her vehicle in low to zero visibility conditions in a desired lane while avoiding collisions. Four areas of research, are described herein: driver assistive displays, the integration of a geospatial database for improved radar processing, snowplow dynamics for slippery conditions, and a virtual bumper based collision avoidance/gang plowing system. (Gang plowing is the flying in formation of snowplows as a means to rapidly clear multilane roads.) Results from this research have vastly improved the performance and reliability of the driver assistive system. Research on the use of a specialized driver assistance system to assist specialty vehicle operators in low visibility conditions, including the design of a custom Head Up Display (HUD) projecting elements of the landscape in proper perspective. Driver assistive displays, the integration of a geospatial database for improved radar processing, snowplow dynamics for slippery conditions, and a virtual bumper based on collision avoidance/gang plowing system are discussed.

This report describes results from a series of experiments using the virtual bumper collision avoidance algorithm implemented on a Navistar tractor cab. The virtual bumper combines longitudinal and lateral collision avoidance capabilities to control a vehicle in normal and emergency situations. A programmable boundary, the virtual bumper, defines a personal space around the host vehicle. Researchers used a radar and a laser range sensor to sense the location of vehicles in front of the truck. Target vehicles that enter the truck's personal space impose a virtual "force" on the host, which in turn modifies the vehicle's trajectory to avoid collisions with objects in the field of view. Researchers tested the virtual bumper longitudinal controller under different driving situations and at different speeds. The experiments included several scenarios: Adaptive Cruise Control, the truck performing a critical stop for a stationary target vehicle, and situations that simulate stop-and-go traffic. Results from the virtual bumper longitudinal experiments were favorable. The algorithm demonstrated robustness to sensor noise and the ability to maintain a safe headway for both normal and emergency driving scenarios. Researchers currently are improving the sensing technology and incorporating a road database, which contains roadside features to greatly reduce, if not eliminate, false target detection.