A Closed-Loop Speed Advisory Model With Driver's Behavior Adaptability for Eco-Driving

Xiang, Xuehai; Zhou, Kun; Zhang, Wei-Bin; Qin, Wenhu; Mao, Qingzhou

doi:10.1109/tits.2015.2443980

Cited by 45 publications

(25 citation statements)

References 27 publications

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“…In congested traffic conditions, the EAD application also worked effectively where preceding queues could be estimated by leveraging real time vehicle detection and signal information system [12]. Moreover, it was also revealed in previous studies that, drivers' behavior adaptability under actual driving conditions also played an important role in the effectiveness of the EAD application [13].…”

Section: Introductionmentioning

confidence: 85%

Early Findings from Field Trials of Heavy-Duty Truck Connected Eco-Driving System

Wang

Amar

Garmon

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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In recent years, the development of connected and automated vehicle (CAV) technology has inspired numerous advanced applications targeted at improving existing transportation systems. As one of the widely studied applications of CAV technology, connected eco-driving takes advantage of Signal Phase and Timing (SPaT) information from traffic signals to enable CAVs to approach and depart from signalized intersections in an energy-efficient manner. However, the majority of the connected eco-driving studies have been numerical or microscopic traffic simulations. Only few studies have implemented the application on real vehicles, and even fewer have been focused on heavy-duty trucks. In this study, we developed a connected eco-driving system and equipped it on a heavy-duty diesel truck using cellular-based wireless communications. Field trials were conducted in the City of Carson, California, along two corridors with six connected signalized intersections capable of communicating their SPaT information. Early results showed the benefits of the system in smoothing the speed profiles of the equipped truck when approaching the connected signalized intersections. arXiv:1908.00541v1 [eess.SY]

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Section: Introductionmentioning

confidence: 85%

Early Findings from Field Trials of Heavy-Duty Truck Connected Eco-Driving System

Wang

Amar

Garmon

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

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“…Eco-driving reduces fuel consumption and emissions without reducing the average speed of vehicles [51,52]. Furthermore, it ensures a safer journey and increases social responsibilities [53].…”

Section: Literature Content Analysis and Synthesizementioning

confidence: 99%

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

et al. 2018

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Road transportation is the main energy consumer and major contributor of ever-increasing hazardous emissions. Transportation professionals have raised the idea of applying the green concept in various areas of transportation, including green highways, green vehicles and transit-oriented designs, to tackle the negative impact of road transportation. This research generated a new dimension called the green driver to remediate urgently the existing driving assessment models that have intensified emissions and energy consumption. In this regard, this study aimed to establish the green driver's behaviors related to fuel saving and emission reduction. The study has two phases. Phase one involves investigating the driving behaviors influencing fuel saving and emission reduction through a systematic literature review and content analysis, which identified twenty-one variables classified into four clusters. These clusters included the following: (i) FE f1 , which is driving style; (ii) FE f2 , which is driving behavior associated with vehicle transmission; (iii) FE f3 , which is driving behavior associated with road design and traffic rules; and (iv) FE f4 , which is driving behavior associated with vehicle operational characteristics. The second phase involves validating phase one findings by applying the Grounded Group Decision Making (GGDM) method. The results of GGDM have established seventeen green driving behaviors. The study conducted the Green Value (GV) analysis for each green behavior on fuel saving and emission reduction. The study found that aggressive driving (GV = 0.16) interferes with the association between fuel consumption, emission and driver's personalities. The research concludes that driver's personalities (including physical, psychological and psychosocial characteristics) have to be integrated for advanced in-vehicle driver assistance system and particularly, for green driving accreditation.

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“…In terms of congested traffic, the EAD application also worked efficiently in which preceding queues were taken into account by adopting real time vehicle detection and signal information system [15]. Moreover, it was also revealed in previous studies that a drivers' behavior adaptability under actual driving conditions also plays an important role in the effectiveness of the EAD application [16], [17].…”

Section: Introductionmentioning

confidence: 98%

Cluster-Wise Cooperative Eco-Approach and Departure Application for Connected and Automated Vehicles Along Signalized Arterials

Wang

Hao

et al. 2018

IEEE Trans. Intell. Veh.

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In recent years, various versions of the Eco-Approach and Departure (EAD) application have been developed and evaluated. This application utilizes Signal Phase and Timing (SPaT) information to allow connected and automated vehicles (CAVs) to approach and depart from a signalized intersection in an energy-efficient manner. To date, most existing work have studied the EAD application from an ego-vehicle perspective (Ego-EAD) using Vehicle-to- Infrastructure (V2I) communication, while relatively limited research takes into account cooperation among vehicles at intersections via Vehicle-to-Vehicle (V2V) communication. In this research, we developed a cluster-wise cooperative EAD (Coop-EAD) application for CAVs to further reduce energy consumption compared to existing Ego-EAD applications. Instead of considering CAVs traveling through signalized intersections one at a time, our approach strategically coordinates CAVs' maneuvers to form clusters using various operating modes: initial vehicle clustering, intra-cluster sequence optimization, and cluster formation control. The novelCoop-EAD algorithm is applied to the cluster leader, and CAVs in the cluster follow the cluster leader to conduct EAD maneuvers. A preliminary simulation study with a given scenario shows that, compared to an Ego-EAD application, the proposed Coop-EAD application achieves 11% reduction on energy consumption, up to 19.9% reduction on pollutant emissions, and 50% increase on traffic throughput, respectively.

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A Closed-Loop Speed Advisory Model With Driver's Behavior Adaptability for Eco-Driving

Cited by 45 publications

References 27 publications

Early Findings from Field Trials of Heavy-Duty Truck Connected Eco-Driving System

Early Findings from Field Trials of Heavy-Duty Truck Connected Eco-Driving System

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

Cluster-Wise Cooperative Eco-Approach and Departure Application for Connected and Automated Vehicles Along Signalized Arterials

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