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Meyer, Rick
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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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Exergy Based Optimal Controller Design of a Spark-Ignition Internal Combustion Engine

CEAS Western Michigan University-Muataz Abotabik, Rick Meyer, Christopher Proctor
  • Technical Paper
  • 2020-01-0250
To be published on 2020-04-14 by SAE International in United States
Internal combustion engine (ICE) control techniques have been developed with only the first law of thermodynamics in mind, e.g. improving thermal efficiency, tracking specific load requirements, etc. The first law of thermodynamics does not account for the losses in work potential that are caused due to the in-cylinder high temperature thermodynamic processes irreversibilities. For instance, up to 25% of fuel exergy or fuel availability may be lost to irreversibilities during the combustion process. The second law of thermodynamics states that not all energy in an energy source is available to do work; its application evaluates the maximum available energy in that source after accounting for the losses caused by the irreversibilities. Therefore, including the exergy in an optimal engine control algorithm may lead to improved ICE thermal efficiencies. In this work, a model predictive controller (MPC) is developed based on the first and second laws of thermodynamics to control a detailed eight-cylinder ICE model developed in GT-Power. To make the controller practically applicable for eventual hardware in the loop (HiL) investigations, the GT-Power model is…
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Observer for Faulty Perception Correction in Autonomous Vehicles

Western Michigan University-Mark Omwansa, Rick Meyer, Zachary Asher, Nick Goberville
  • Technical Paper
  • 2020-01-0694
To be published on 2020-04-14 by SAE International in United States
Operation of an autonomous vehicle (AV) carries risk if it acts on inaccurate information about itself or the environment. The perception system is responsible for interpreting the world and providing the results to the path planning and other decision systems. The perception system performance is a result of the operating state of the sensors, e.g. is a sensor in fault or being adversely affected by the weather or environmental conditions, and approach to sensor measurement interpretation. We propose a trailing horizon switched system observer that minimizes the difference between reference tracking values developed from sensor fusion performed at an upper level and the values from a potentially faulty sensor based upon the convex combination of different sensor observation model outputs; the sensor observations models are associated with different sensor operating errors. The preferred observer target is a stationary landmark so as to remove additional uncertainty resulting from tracking of moving targets. Results for five scenarios show the observer identifies the appropriate sensor model in no more than a few sample intervals.
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Analysis of LiDAR and Camera Data in Real-World Weather Conditions for Autonomous Vehicle Operations

Western Michigan University-Nick Goberville, Mohammad El-Yabroudi, Mark Omwanas, Johan Rojas, Rick Meyer, Zachary Asher, Ikhlas Abdel-Qader
  • Technical Paper
  • 2020-01-0093
To be published on 2020-04-14 by SAE International in United States
Autonomous vehicle technology has the potential to improve the safety, efficiency, and cost of our current transportation system by removing human error. With sensors available today, it is possible for the development of these vehicles, however, there are still issues with autonomous vehicle operations in adverse weather conditions (e.g. snow-covered roads, heavy rain, fog, etc.) due to the degradation of sensor data quality and insufficiently robust software algorithms. Since autonomous vehicles rely entirely on sensor data to perceive their surrounding environment, this becomes a significant issue in the performance of the autonomous system. The purpose of this study is to collect sensor data under various weather conditions to understand the effects of weather on sensor data. The sensors used in this study were one camera and one LiDAR. These sensors were connected to an NVIDIA Drive Px2 which operated in a 2019 Kia Niro. Two custom scenarios (static and dynamic objects) were chosen to collect sensor data operating in four real-world weather conditions: fair, cloudy, rainy, and light snow. An algorithm developed herein was used…