This Grant Opportunities for Academic Liaison with Industry (GOALI) project advances a research foundation for effective systems to model, predict, and mitigate the effects of tire blowout on automobile dynamics. The goal of this project is to substantially improve road safety, especially as driver-assist systems or full autonomy become more widespread in personal vehicles. Three specific research thrusts are addressed. The first research thrust is an in-depth model of the blowout event itself -- essentially a "super slow-motion" look at the changes that occur as the tire rapidly loses its integrity. This model is embedded in a vehicle simulation software, to predict the dynamic response of various vehicles, under different ambient conditions. Because this high-fidelity modeling approach splits the brief duration of the blowout into many tiny time intervals, the resulting computer software is too slow to be used during an actual event. Therefore, the second research thrust is to create a model of tire blowout that can be run by an on-board computer to help keep the car under control. An "impulsive" control model condenses the blowout event into a single jump change in the vehicle status. An on-board computer uses the impulsive model to interpret sensor readings, determine the type of blowout that has occurred, and predict the subsequent motion of the car. Finally, the on-board computer uses the model to keep the car under control until it can be brought to a safe stop. This controller is appropriate for self-driving vehicles, however the near future is more likely to see deployment of partial autonomy, in which a driver-assist feature shares some degree of control with a human operator. For partially autonomous situations the on-board controller must be able to predict and accommodate operator inputs. Therefore, the third research thrust of the project is the creation of a human-behavior model to be integrated with the vehicle control system, that will enable effective shared control and ensure safe operation over a wide range of driver skills and emotional states. Each year tens of thousands of accidents and hundreds of deaths could be prevented by a successful outcome to this project. The transition of the results to commercial practice will be facilitated by a research partnership with General Motors, who will provide subject-matter expertise, and validate scale-model laboratory experiments with full-scale testing at GM facilities.
The first of three tasks will advance the research team's preliminary efforts towards an experimentally validated, high-fidelity model of tire blowout. The model features a two-phase analysis of forces during blowout, with the first phase accounting for the initial collapse of the tire, and the second phase accounting for the wheel behavior after the rim contacts the ground. This high-fidelity model will be parametrized by quantities such as tire pressure, tire size, cornering stiffness, and vehicle geometry, and will include an analysis of the sensitivity of vehicle behavior to those parameters. The large time-scale separation between the dynamics of the tire blowout and the preceding and subsequent vehicle dynamics makes this model too unwieldy for real-time use. Therefore, the second task is to derive and validate an impulsive model of the vehicle dynamics before and after tire blowout. That is, the dynamics of the blowout event itself are ignored, and its effects are gathered into an instantaneous impulsive force applied at the stricken tire, plus an associated step change in the vehicle parameters. Versions of the model will be obtained for two types of estimators -- the case where the identity of the blown-out tire is known, and the case for which it must be inferred. It is of interest to consider both fully autonomous and partially autonomous vehicles. For the latter, a human driver shares control with an automatic controller. Under shared control, the automatic controller should be able to interpret and predict the human driver input, in order to provide an appropriate stabilizing response. Thus the third research task is to create a model of human behavior, implementable by the vehicle controller. The envisioned model represents the dynamics of human response to lane departure error using a third-order transfer function with time delay. Subsequently, the project will derive controllers to ensure safe lane maintenance after tire blowout for both automated and partially automated vehicles. Innovations in this task include the derivation of dynamic models combining impulsive and continuous controls and disturbances. The project will also develop tools of impulsive observability to describe conditions under which the blowout characteristics may be inferred from sensor measurements.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
