The surface condition of a road is critical for driving safety, particularly during adverse weather. Wet and icy roads occur only 3 percent of the time but account for more than 18 percent of crashes. The project will develop a model to estimate tire-pavement friction based on the fusion of tire and vehicle sensors with advanced algorithms. The ability to collect and evaluate such information in real time will help drivers make better driving decisions. Transportation agencies will also be able to design proactive strategies. For example, agencies could use the friction estimates to identify and treat accident-prone "hot spots" before crashes happen, or to prioritize the most critical road segments to treat first during a winter storm. The resulting enhanced decision-making can help reduce the number of crashes and save lives.
This research will combine responses from tire and vehicle sensors to develop models to evaluate the friction levels between tires and pavement. The project has two specific goals: (1) to develop a theory to describe the tire-road contact mechanics and the resulting friction properties; and (2) to conduct experiments to validate the proposed model. The project fills a critical gap in the development of mathematical models and algorithms for predicting tire-pavement contact and friction. The project also integrates knowledge from mechanical engineering and civil engineering to advance the techniques for evaluating tire-pavement friction. Available, real-time, vehicle-based estimates of hazardous driving conditions will help improve operations in transportation agencies and driving safety.
