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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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Effects of a Probability-Based Green Light Optimized Speed Advisory on Dilemma Zone Exposure

Indiana Department of Transportation-James Sturdevant
Purdue University-Enrique Saldivar-Carranza, Howell Li, Woosung Kim, Jijo Mathew, Darcy Bullock
  • Technical Paper
  • 2020-01-0116
To be published on 2020-04-14 by SAE International in United States
Green Light Optimized Speed Advisory (GLOSA) systems have the objective of providing a recommended speed to arrive at a traffic signal during the green phase of the cycle. GLOSA has been shown to decrease travel time, fuel consumption, and carbon emissions; simultaneously, it has been demonstrated to increase driver and passenger comfort. Few studies have been conducted using historical cycle-by-cycle phase probabilities to assess the performance of a speed advisory capable of recommending a speed for various traffic signal operating modes (fixed-time, semi-actuated, and fully-actuated). In this study, a GLOSA system based on phase probability is proposed. The probability is calculated prior to each trip from a previous week’s, same time-of-day (TOD) and day-of-week (DOW) period, traffic signal controller high-resolution event data. By utilizing this advisory method, real-time communications from the vehicle to infrastructure (V2I) become unnecessary, eliminating data-loss related issues. The effects of three different advice approaches (conservative, balanced, and aggressive) on dilemma zone exposure are analyzed. Proof of concept is carried out by simulating drives through a test-route composed of an arterial that…
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Hardware-in-the-Loop and Road Testing of RLVW and GLOSA Connected Vehicle Applications

Camp LLC-Jayendra Parikh
Ford Motor Co., Ltd.-Alexander Katriniok
  • Technical Paper
  • 2020-01-1379
To be published on 2020-04-14 by SAE International in United States
This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections. This same approach of HIL testing followed by testing in real intersections using our experimental vehicle is later extended to the GLOSA application. The GLOSA application that is tested in this paper has both an optimal speed advisory for passing at the green light and also includes a…
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Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction

Ford Motor Company-Ashley Wiese, Zeng Qiu, Julia Buckland
University of Michigan-Qiuhao Hu, Mohammad R. Amini, Yiheng Feng, Zhen Yang, Hao Wang, Ilya Kolmanovsky, Jing Sun
  • Technical Paper
  • 2020-01-0590
To be published on 2020-04-14 by SAE International in United States
Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications-based information, is difficult and error-prone.The multiple timescales inherent to power and thermal systems call for a variable timescale optimization framework with access to short- and long-term vehicle speed preview. To this end, a model predictive controller (MPC) with a multi-range speed preview for integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs) is presented in this paper. The MPC is formulated…
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Performance Evaluation of the Pass at Green Connected Vehicle V2I Application Using Simulation, Dynamometer and Track Testing

Ohio State University-Ozgenur Kavas-Torris, Mustafa Ridvan Cantas, Sukru Yaren Gelbal, Levent Guvenc
  • Technical Paper
  • 2020-01-1380
To be published on 2020-04-14 by SAE International in United States
In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies, such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication. As the world of transportation gets more and more connected through these technologies, the need to implement algorithms with V2I capability is amplified. In this paper, an algorithm called Pass at Green (PaG), utilizing V2I to modify the speed profile of a vehicle to decrease fuel consumption has been studied. PaG uses Signal Phase and Timing (SPaT) information acquired from upcoming traffic lights, which are the current phase of the upcoming traffic light and the remaining time that the phase stays active. Then, PaG modifies the speed of the vehicle by accelerating, keeping its speed constant or decelerating to decrease fuel consumption, minimize idling time and reduce the likelihood of catching a red light in an intersection. As presented in this paper, the fuel economy benefit achieved by the PaG algorithm was…
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Leverage wireless technologies in timber harvesting to enhance operational productivity and business profitability

John Deere-Ishani Pandit, Suchitra Iyer
  • Technical Paper
  • 2020-01-1378
To be published on 2020-04-14 by SAE International in United States
Growing needs of forestry products; primarily wood followed by pulp and paper industry have mechanized the process of harvesting timber in most part of world. Such job sites have several machines and vehicles working together to harvest and transport the logs. Timber logging is very similar to crop harvesting with longer harvesting cycle and hence it is critical that every part of it is effectively utilized; timely harvest and transport to factories play an important role. Traditionally, these areas have had little cellular connectivity, restricting communication between operators, machines, land owners and factories. With better connectivity, it will be easier to monitor and operate job sites for example if skidder would know how many trees are felled, how many logs and bunches are created and where they are kept; it would reduce time and fuel spent in searching for logs. Also, with better communication between machines, skidder would know when to pick up logs and avoid longer wait time. Timely pick up of felled trees is critical in ensuring log quality. With upcoming wireless technologies…
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Connected Vehicles - An Ecosystem for Services

Automotive Software-Prabha Baragur Venkataram
  • Technical Paper
  • 2019-28-2447
Published 2019-11-21 by SAE International in United States
This paper outlines the different aspects of the Connected Vehicle concept. The blocks required to implement a Connected Vehicle infrastructure is also discussed in detail.Two main types of short-range wireless communication are discussed in Connected Vehicles context namely Vehicle-to-Vehicle (V2V), and Vehicle-to-Infrastructure (V2I) communication.An overview of the evolution of the Connected Vehicle and its operational aspects are presented together with its application. The impacts and potential operational benefits of the Connected Vehicle are discussed.The various challenges to architect non-functional requirements in the case of Connected Vehicle technology are identified and discussed.
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Active Safety System for Connected Vehicles

SAE International Journal of Connected and Automated Vehicles

Michigan State University, USA-Hothaifa Al-Qassab, Su Pang, Mohammed Al-Qizwini, Daniel Kent, Hayder Radha
  • Journal Article
  • 12-02-03-0013
Published 2019-10-14 by SAE International in United States
The development of connected-vehicle technology, which includes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, opens the door for unprecedented active safety and driver-enhanced systems. In addition to exchanging basic traffic messages among vehicles for safety applications, a significantly higher level of safety can be achieved when vehicles and designated infrastructure locations share their sensors’ data. In this article, we propose a new system where cameras installed on multiple vehicles and infrastructure locations share and fuse their visual data and detected objects in real time. The transmission of camera data and/or detected objects (e.g., pedestrians, vehicles, cyclists, etc.) can be accomplished by many communication methods. In particular, such communications can be accomplished using the emerging Dedicated Short-Range Communications (DSRC) technology. In our proposed system the vehicle receiving the visual data from an adjacent vehicle fuses the received visual data with its own camera views to create a much richer visual scene. We conducted several experiments across a pair of vehicles equipped with DSRC devices and our proposed system. These experiments demonstrated that our system achieves high accuracy,…
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Data connectivity in HARSH ENVIRONMENTS

SAE Truck & Off-Highway Engineering: August 2019

Christian Manko
  • Magazine Article
  • 19TOFHP08_03
Published 2019-08-01 by SAE International in United States

Ensuring high-speed data transmission requires OEM designers to think more about components, placement and the impact of environmental conditions early in design.

Technology advances are increasingly bringing a new level of connectivity to industrial and commercial vehicles. Customers are demanding functionality that automates or enhances operational tasks to increase driver productivity and safety and, in many cases, also brings down total cost of ownership.

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Use Cases for Communication Between Plug-in Vehicles and the Utility Grid

Hybrid - EV Committee
  • Ground Vehicle Standard
  • J2836/1_201907
  • Current
Published 2019-07-15 by SAE International in United States
This SAE Information Report establishes Use Cases for communication between plug-in electric vehicles (PEVs) and the electric power grid, for energy transfer and other applications.
This content contains downloadable datasets
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