SAE Technical Paper Series 2018
DOI: 10.4271/2018-01-0593
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Towards Improving Vehicle Fuel Economy with ADAS

Abstract: Modern vehicles have incorporated numerous safety-focused advanced driver-assistance systems (ADAS) in the last decade including smart cruise control and object avoidance. In this article, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy (FE). We combine ADAS sensor data with a previously developed prediction model, dynamic programming (DP) optimal energy management control, and a validated model of a 20… Show more

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Cited by 13 publications
(4 citation statements)
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References 26 publications
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“…In 2018, OSU announced two research results, one is of the performance improvement of coasting in N gear [18] ,and the other is about the nonlinear simulation of predictive cruise control [19] . By modifying 2010 Prius, CSU(Colorado State University) inquired several research topics, including the speed prediction by V2V-based technology vehicle to improve HEV fuel economy [20] , ADAS to improve vehicle fuel economy [21] , and improve the accuracy of prediction on fuel economy by control optimize [22] .…”
Section: Researches and Activities In Usmentioning
confidence: 99%
“…In 2018, OSU announced two research results, one is of the performance improvement of coasting in N gear [18] ,and the other is about the nonlinear simulation of predictive cruise control [19] . By modifying 2010 Prius, CSU(Colorado State University) inquired several research topics, including the speed prediction by V2V-based technology vehicle to improve HEV fuel economy [20] , ADAS to improve vehicle fuel economy [21] , and improve the accuracy of prediction on fuel economy by control optimize [22] .…”
Section: Researches and Activities In Usmentioning
confidence: 99%
“…Based on previous research identifying aspects of real-world driving that are most important for prediction [51], it was determined that the ADAS detection objective should only include identification of the state of the traffic light, identifying vehicle speed changes from the vehicle directly in front, identification of stop sign location, and identification of turn lanes. An analysis of automated ADAS detection algorithms and a comparison to ADAS ground truth is available in a separate article [54]. ADAS usage has the advantage of providing detailed drive cycle prediction information for the upcoming 1-100 seconds.…”
Section: Optimal Energy Management Strategy Simulationsmentioning
confidence: 99%
“…POEMSs use predicted vehicle velocity (enabled through ADAS [15] and connectivity) as an input to optimal control. The optimal solution output is then used as an input to the vehicle plant, ideally an HEV or PHEV due to the additional operational degrees of freedom [16].…”
Section: Introductionmentioning
confidence: 99%
“…In 2015, the advantages of ANN prediction were shown in [21,22]. In 2017 and 2018, a series of studies [15,17,29,30] experimented with different data streams to optimize prediction with a shallow ANN. In 2019, more modern machine learning techniques were introduced into the field in [31] where reinforcement learning was used along with traffic data to train an ANN to produce optimal controls for a power-split hybrid.…”
mentioning
confidence: 99%