For many research problems, controlling and estimating the position of the mobile elementswithin an environment is desired. Realistic mobile environments are unstructured, but sharea set of common features, such as position, speed, and constraints on mobility. To estimate within these real-world environments requires careful selection of the best-suited estimation tools and software and hardware technologies. This dissertation discusses the design and implementation of applied estimation infrastructures which overcome the challenges of realworld deployments.
Distracted driving is still a major concern on roadways today. According to the US Department ofTransportation, in 2011, 387,000 people were injured in crashes involving a distracted driver.While there has been tremendous work in vehicular safety systems, as the velocity of the vehicleincreases, threat assessment becomes more difficult to predict. In this regime, understanding howthe driver behaves is critical in determining the safety of the vehicle. This work presents aframework for incorporating human driver modeling with a controller to provide asemi-autonomous system for vehicle safety. We describe how the system is built, present metricsto evaluate its utility and conduct real-time experiments with a safety controller to verify theutility of incorporating the driver’s behavior while designing a semi-autonomous system.