Prediction-Based Eco-Approach and Departure at Signalized Intersections With Speed Forecasting on Preceding Vehicles

Ye, Fei; Hao, Peng; Qi, Xuewei; Wu, Guoyuan; Boriboonsomsin, Kanok; Barth, Matthew

doi:10.1109/tits.2018.2856809

Cited by 76 publications

(38 citation statements)

References 29 publications

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“…It has been proven that the vehicle energy consumption highly relies on the acceleration/deceleration and speed profile [10]. The optimization problem becomes a constrained nonlinear programming problem.…”

Section: B Qp-based Optimal Trajectory Planningmentioning

confidence: 99%

Bi-level Optimal Edge Computing Model for On-ramp Merging in Connected Vehicle Environment

Guo

Kim

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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The coordinated on-ramp merging is one of the most common but critical vehicular applications that require complex data transmission and low-latency communication in the Connected and Automated Vehicles (CAVs) environment. An effective way to address on-ramp merging is to leverage the edge computing to optimize the coordination among vehicles to achieve overall minimum vehicle travel time and energy consumption. In this study, we propose an Bi-level Optimal Edge Computing (BOEC) model for on-ramp merging in the CAVs environment to optimize both merge time and vehicle trajectory. The simulation results show that the proposed BOEC model achieves great benefits in vehicle mobility, energy saving and air pollutant emission reduction by providing an energy-efficient trajectory following the optimal merge time without compromising safety.

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Section: B Qp-based Optimal Trajectory Planningmentioning

confidence: 99%

Bi-level Optimal Edge Computing Model for On-ramp Merging in Connected Vehicle Environment

Guo

Kim

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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“…In order to guide the implementation or test the robustness of these eco-driving approaches in complex traffic conditions, some studies attempted to establish a relationship between their environmental benefits and traffic-related factors [16]. Typical traffic-related factors include the congestion level (CL) and market penetration rate (MPR) of vehicles controlled by eco-driving controllers [18,20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…With emerging connected and automated vehicle (CAV) technology, a brand new fuel efficiency improvement technology known as "eco-driving" has become an emerging research focus [6]. Most existing eco-driving research can be categorized into eco-driving on the freeway [7][8][9][10][11] and eco-driving on signalized arterials [12][13][14][15][16][17][18][19]. Eco-driving on the freeway mainly focuses on smoothing the vehicles' speed profile to reduce traffic oscillations which can cause extra energy consumption and emissions.…”

Section: Introductionmentioning

confidence: 99%

“…These strategies are not feasible for real-world implementation in the near future due to their poor robustness against the impedance from conventional vehicles and the randomness of traffic [16]. To overcome this drawback, queue theory and car following theory were introduced into some eco-driving studies on signalized arterials to predict the location and motion of conventional vehicles [16][17][18][19][20][21]. It is expected that these eco-driving controllers will be functional in a partially connected and automated vehicle environment and can be implemented as long as CAV technology is introduced in the near future [17].…”

Section: Introductionmentioning

confidence: 99%

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An Extensive Investigation of an Eco-Approach Controller under a Partially Connected and Automated Vehicle Environment

Jiang

Park

et al. 2019

Sustainability

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This study evaluated the performance of an eco-approach control system at signalized intersections under a partially connected and automated vehicle (CAV) environment. This system has the first eco-approach controller able to function with the existence of surrounding human-driven traffic. A previous evaluation only confirmed its benefits. The purpose of this study was to conduct a further extensive test on the controller to identify room for improvement. Two different networks were tested, including an isolated signalized intersection and a corridor with two signalized intersections. The measures of effectiveness (MOEs) adopted were throughput and fuel consumption. All the before-and-after MOEs were compared using t-tests. The results indicate that the controller generally improved the fuel efficiency without harm to the mobility, and its environmental performance was affected by the minimum CAV speed, green ratio, congestion level, and marker penetration rate of CAVs. A detailed investigation revealed that no significant environmental benefit was generated under high congestion levels when the minimum speed of CAVs was more than 20 mph, and the shockwaves caused by the eco-approach control may result in a gating effect that reduces the throughput at the upstream intersection of the corridor under high congestion levels.

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“…However, early development and deployment of connected eco-driving technology mainly focused on optimizing vehicle dynamics (VD) [4]- [8] and powertrain (PT) [9] operation independently, and therefore there exists untapped potential to further improve vehicle fuel efficiency through a simultaneous optimization of both VD&PT control. Therefore, this paper proposes an advanced simulation framework of an integrated vehicle-powertrain ecooperation solution for an electric bus with state-of-theart Connected and automated vehicles (CAV) technologies, aiming at improving vehicle energy efficiency and reducing tailpipe emissions.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Simulation Framework of an Integrated Vehicle-Powertrain Eco-Operation System for Electric Buses

Hao

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Activities of transit buses traveling along arterial roads and city streets consist of frequent stops and idling events at many predictable occasions, e.g., loading/unloading passengers at bus stops, approaching traffic signals or stop signs, and going through recurrent traffic congestion, etc. Besides designing transit buses with electric powertrain systems that can save a noticeable amount of energy thanks to regenerative breaking, this urban traffic environment also unfolds a number of opportunities to further improve their energy efficiency via vehicle connectivity and autonomy. Therefore, this paper proposes a complete and novel simulation framework of integrated vehicle/powertrain ecooperation system for electric buses (Eco-bus) by co-optimizing the vehicle dynamics and powertrain (VD&PT) controls. A comprehensive evaluation of the proposed system on mobility benefits and energy savings has been conducted over various traffic conditions. Simulation results are presented to showcase the superiority of the proposed simulation framework of the Eco-bus compared to the conventional bus, particularly in terms of mobility and energy efficiency aspects.

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Prediction-Based Eco-Approach and Departure at Signalized Intersections With Speed Forecasting on Preceding Vehicles

Cited by 76 publications

References 29 publications

Bi-level Optimal Edge Computing Model for On-ramp Merging in Connected Vehicle Environment

Bi-level Optimal Edge Computing Model for On-ramp Merging in Connected Vehicle Environment

An Extensive Investigation of an Eco-Approach Controller under a Partially Connected and Automated Vehicle Environment

An Advanced Simulation Framework of an Integrated Vehicle-Powertrain Eco-Operation System for Electric Buses

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