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Gaikwad, Tushar
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Vehicle Velocity Prediction Using Artificial Neural Network and Effect of Real World Signals on Prediction Window.

Colorado State University-Aaron Rabinowitz, Thomas Bradley
Western Michigan University-Tushar Gaikwad, Farhang Motallebiaraghi, Zachary Asher, Alvis Fong, Rick Meyer
  • Technical Paper
  • 2020-01-0729
To be published on 2020-04-14 by SAE International in United States
Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide the highest velocity prediction fidelity. We developed LSTM deep neural network which uses different groups of datasets collected in Fort Collins. Synchronous data was gathered using a test vehicle equipped with sensors to measure ego vehicle position and velocity, ADAS-derived near-neighbor relative position and velocity, and infrastructure-level transit time and signal phase and timing. Effect of different group of datasets on forward velocity prediction window of 10, 15, 20 and 30 seconds is studied. Developed algorithm is tested…
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Synchronous and Open, Real World, Vehicle, ADAS, and Infrastructure Data Streams for Automotive Machine Learning Algorithms Research

Colorado State University-Aaron I. Rabinowitz, Samantha White, Thomas Bradley
Western Michigan University-Tushar Gaikwad, Zachary Asher
  • Technical Paper
  • 2020-01-0736
To be published on 2020-04-14 by SAE International in United States
The suite of CAVs-derived data streams have been demonstrated to enable improvements in system-level safety, emissions and fuel economy. This describes the gathering, processing, and use of on-road data collected from probe vehicles in Fort Collins, Colorado. Several synchronous datasets were gathered using a test vehicle equipped with sensors to measure ego vehicle position and velocity, ADAS-derived near-neighbor relative position and velocity, and infrastructure-level transit time and signal phase and timing. The datasets in both raw and data-fused formats is made available to the research community. The utility of these types of open data projects is briefly demonstrated by using them in the applications of vehicle velocity prediction, and real-time fuel economy modeling.