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On-board diagnostics (OBD) data contain valuable information including real-world measurements of vehicle powertrain parameters. These data can be used to gain a richer data-driven understanding of complex physical phenomena like emissions formation during combustion. In this study, we develop a physics-based machine learning framework to predict and analyze trends in engine-out NOx emissions from diesel and diesel-hybrid heavy-duty vehicles. This model differs from black-box machine learning models presented in previous literature because it incorporates engine combustion parameters that allow physical interpretation of the results. Based on chemical kinetics and the characteristics of diffusive combustion, NOx emissions from compression ignition engines primarily depend non-linearly on three parameters: adiabatic flame temperature, the oxygen concentration in the cylinder when the intake valves are closed, and combustion time duration.

An IVHS (Intelligent Vehicle Highway System) navigation system obtains information from road sensors, city maps and event schedules, and generates information for drivers. We address two aspects of navigation in IVHS: finding a path and evaluating a given path. Finding a path between a starting point and the destination is based on heuristic search procedures. Evaluation of a given path is modeled as a path query. We use a new access method, called MoBiLe File[18], for efficient map storage and access. We propose a hierarchical path planning algorithm which is capable of finding optimal paths while avoiding obstacles. We contribute obstacle avoidance heuristics for faster computation of paths between two points.