Engineering topics
Publications and source records attributed to Kumar, Maya.
This dataset was created by recording CAN and GPS data from a single vehicle driving on I-24. The dataset includes values for Time, Velocity, Acceleration, Space Gap, Lateral Distance, Relative Velocity, Longitude GPS, Latitude GPS and more. This empirical dataset is useful for understanding/simulating real vehicle trajectories and vehicle controller performance.
This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems. This framework serves as a key building block in developing control strategies for human-in-the-loop traffic flow smoothing on real highways. In this contribution, we outline the fundamental merits of integrating vehicle dynamics and energy modeling into a single framework, and we demonstrate the energy impact of sparse flow smoothing controllers via simulation results.