The research objective of this award is to investigate the next generation, proactive, driver-assist active safety control systems (ASCS) for commercial passenger vehicles. The main novel ingredient over existing methods is the adaptation of the ASCS specifications and operation to the individual driver habits and driving skills (e.g., aggressive or timid), his/her current cognitive state (e.g., attentive or not). By using recently developed techniques from the field of computational neuroscience and adaptive control theory, this research will develop algorithms that will capture the state of the driver, the vehicle and the environment from automotive sensors and behavioral (e.g., eye movement) measurements that will be subsequently used to adapt and customize the ASCS to particular situations so as to achieve maximum performance (e.g., minimum stopping distance during emergency braking, etc). This research will take advantage of recent advances in sensor technology, which has led to the reliable fusion of data, so as to provide situational awareness for the vehicle and the persistent monitoring of the (re)actions of the driver.
If successful, this research will enable new levels of performance for the current active safety systems for passenger vehicles, thus leading to decreased accident rates, increased comfort and improved fuel economy. Graduate and undergraduate engineering students as well as local high school teachers will benefit from their involvement in this research through NSF's REU and RET projects and through Georgia Tech?s PURA and Dash undergraduate research fellowship programs. Undergraduate and high-school minority students will actively participate in data collection and analysis. Under-represented groups will be particularly targeted for participation in the research activities under this award, directly through active recruitment and indirectly through the collaboration with the industry partner, Ford Motor Company, e.g., in the form of summer internships.
