Donath, Max

Snowplow operators are often tasked with clearing snow from roadways under challenging conditions. One such situation is low visibility due to falling or blowing snow that makes it difficult to navigate, stay centered in the lane, and identify upcoming hazards. To support snowplow operators working in these conditions, University of Minnesota researchers developed a snowplow driver-assist system that provides the operator with visual and auditory information that is suitable for low-visibility situations. A lane-guidance system uses high-accuracy Global Navigation Satellite System (GNSS) and maps of the roadway to provide information to drivers about their lateral positions. A forward-obstacle-detection system uses forward-facing radar to detect potential hazards in the roadway. The design of the system, and in particular its interface, is guided by extensive user testing to ensure the system is easy to understand, easy to use, and well liked among its users. The system was deployed in two phases over the 2020-2021 and 2021-2022 winter seasons. In total, nine systems were deployed on snowplows across Minnesota, four in the first winter season and an additional five in the second. Participating truck stations represented all eight MnDOT districts as well as Dakota County. Over the course of the deployment, additional user feedback was collected to identify system strengths and areas for improvement. The system was found to be a cost-effective addition to snowplows that increase driver safety, reduce plow downtime, and increase driver efficacy for plowing operations, thus providing support to operators working in demanding, low-visibility conditions.

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.

This report summarizes research on a new collision avoidance strategy, the 'virtual bumper.' The research involves development and simulation testing of the virtual bumper, a two-dimensional control strategy that provides steering, throttle, and braking actuation to maneuver a vehicle in a dynamic environment with the goal of avoiding obstacles and other vehicles. The concept applies to both normal and emergency driving conditions. Under all circumstances, the virtual bumper incorporates vehicle dynamic limits to ensure that the control commands are within safe levels. The virtual bumper will attempt to avoid a collision and will, at least, minimize the magnitude of an unavoidable collision. To test the functionality of the virtual bumper, researchers evaluated several driving scenarios. The scenarios consider both normal driving situations and emergency driving conditions. The normal driving scenarios demonstrated that the control algorithm operates the vehicle similar to the way a human would. This is important because a comfortable and predictable (i.e., intuitive) system response is required for achieving driver acceptance. The emergency scenarios demonstrated that the strategy is capable of reacting appropriately while maintaining safe acceleration/deceleration levels for the vehicle. This evaluation showed that the virtual bumper can provide safe vehicle control for a broad range of driving situations.

This report summarizes the work performed during the 18-month period ending in December 1997. Researchers investigated the use of differential global positioning systems (GPS), inertial measurement, and other sensing technologies as the basis of a system that would prevent crashes. Such a system attempts to control the vehicle if it leaves the lane because the driver is incapacitated. The report includes in its appendices related work on driver fatigue and a bibliography on the effect of drugs and alcohol on driving behavior. The long-term goal of this research involves development of a "driver-centered" vehicle control system capable of providing lane-keeping feedback to the driver, and, if necessary, of imposing aggressive intervention strategies to take over control of the vehicle, steer it to a safe position on the shoulder, and stop it. This research also targets the development of "driver assistive" technologies--such as Heads Up Display and torque feedback supplied by the steering wheel--which provide information to the driver without necessarily requiring computer control of the vehicle. The highlight achievement during this funding period has been the successful demonstration of a GPS-based automated lane-keeping mode of a tractor-trailer on the Minnesota Road Research Project (Mn/ROAD) test track. The report concludes with a strategy for pursuing future deployment.

The SAFETRUCK program focuses on preventing accidents on rural highways, especially those associated with run-off-the-road incidents and driver fatigue, by giving the vehicle the ability to steer to the side of the road and come to a safe stop if the driver falls asleep or is otherwise incapacitated. Researchers have equipped a Navistar 9400 series class 8 truck tractor with the sensors and control computers necessary to perform this task. Designing the controller that will steer the truck requires a mathematical model of the lateral response of the truck to steering inputs. In this project, researchers developed a lateral dynamic model by incorporating second order dynamics into the steering axle tires. Simulation of the resulting models indicated dynamic behavior that was close to the experimental data for speeds between 15 and 30 miles per hour. This is the first time that a lateral dynamic model of a truck has been experimentally verified. Both models, however, resulted in experimentally determined values for steering axle cornering stiffness that were considerably smaller than published values for the Goodyear G 159 tires on the truck.

An ongoing research project examines guidance systems, which can take over control of a vehicle if the driver becomes incapacitated. Part of this project includes an evaluation of a Differential Global Positioning System (DGPS) for vehicle-based lane sensing. This report documents the results of tests of the 5 Hz NovAtel RT20 DGPS receiver. A series of 32 static tests found the overall mean and standard deviation for the offset errors within specifications. In a series of dynamic tests, in which the vehicle was driven around the track at speeds of 20-35 miles per hour, after removing the effect of the GPS receiver's latency, the DGPS determined position exhibited a mean offset error of -17.3 cm (-6.82 in) and a mean standard deviation of 25.5 cm (10.1 in) in the direction of vehicle motion. In the direction perpendicular to vehicle motion, the mean offset was 4.57 cm (1.8 in) with a mean standard deviation of 39.6 cm (15.6 in). With no overhead obstructions in these tests, continuous satellite lock was possible. Tests at higher speeds based on a more accurate methodology are planned for the future.

This report presents results of the research performed on the Autonomous Land Experimental Vehicle (ALX) at the University of Minnesota. ALX autonomously follows roadways through the use of visual perception, and executes obstacle detection and collision avoidance through the use of ultrasonic sonar range sensors. This report describes the ALX embedded real-time control system based on a multi-processor, multi-tasking architecture, and presents algorithms used for visual perception, path tracking, position estimation, obstacle detection, and collision avoidance. Computer simulation and experimental results also are presented.

This report provides an overview of autonomous vehicle technology, specifically focusing on sensing and control technologies. It resulted from safety issues at the Mn/ROAD high-load, low-volume pavement test facility. Appropriate technology helps ensure the safety of the truck driver that provides loads to the pavement and the safety of traffic on I-94. Researchers currently are working to provide a semi tractor capable of driver-supervised autonomous operation at the Mn/ROAD facility. Such a driver-supervised system will allow the truck driver to monitor the operation of the automatic control system actively guiding the truck and will allow the driver to take control from the control computer when desired.

With interest in collision avoidance technology for highway vehicles on the rise, this report presents an overview of current collision avoidance technology, the technical work required to bring these systems to a commercially viable product, and the societal issues that need addressing before wide-scale deployment can occur. Many questions remain about the benefits of deploying such systems, the costs, the effect of these systems on drivers, and the steps necessary to effectively regulate vehicles equipped with such systems. In addition to technical aspects, the report also discusses the issues that society will face during development and deployment of these systems, which may prove bigger impediments to deployment than technical issues. The report also recommends a research plan to perform fair, unbiased evaluations of emerging collision avoidance technology.

Recent advances in MIMIC (Millimeter Monolithic Integrated Circuit) radar technology play an important role in the development of automated highway systems and automated vehicle control systems. This report presents results of a preliminary investigation into MIMIC-based automotive radar technology and makes recommendations for hardware evaluation. MIMIC technology integrates much of the radar transmitted, receiver, and signal processing hardware onto a one- or two-piece chip set. Massive integration leads to lower manufacturing costs and lower product costs. Moreover, this integration reduces the size of hardware, allowing the radar components to be installed in the vehicle without the need for significant modifications. As radar systems become smaller and cheaper, the demand for these systems will increase. Radar systems affect both the vehicles so equipped and other vehicles within a reasonable proximity. Before vehicles equipped with radar systems travel on public roads, their effects on traffic flow and highway safety must be investigated so that proper regulations can be developed and enforced.