Impact of Automated Vehicle Eco-Approach on Human-Driven Vehicles

Cui, Lian; Jiang, Huifu; Park, Byungkyu Brian; Byon, Young-Ji; Hu, Jia

doi:10.1109/access.2018.2874761

Cited by 20 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, insights into the energetic impacts of CAV on human drivers will be critical in systematically assessing the potential influences of adopting CAV in real traffic. In [11], the authors design a driver-in-the-loop experiment for mixed traffic to evaluate the impacts of the CAV equipped with the eco-approach on the fuel consumption of a following human-driven vehicle. The results show that CAV with eco-approach can help the following human-driven vehicle to achieve a 6% fuel economy improvement.…”

Section: Introductionmentioning

confidence: 99%

Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation

Ozkan

2021

IEEE Access

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation

Ozkan

2021

IEEE Access

View full text Add to dashboard Cite

“…Some advanced eco-driving approaches can even generate significant benefits as long as there are CAVs [16,17], which indicates that these approaches can be implemented with a low CAV MPR and could be implemented in the near future. The interactions between human-driven vehicles and CAVs show that CAVs have a positive effect on following conventional vehicles [24,25]. That is to say, the conventional vehicles could obtain approximately optimal speed profiles by following their preceding CAVs to reduce fuel consumption and emissions [16,17].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Jiang

Park

et al. 2019

Sustainability

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The position, spatial recurrence and direction selectivity properties are accepted to have a significant job in visual discernment. The paper proposes a novel face portrayal and acknowledgment approach by investigating data mutually in picture space, scale and direction areas [6]. The face picture is first deteriorated into various scale and direction reactions by convolving multi-scale and multi-direction Gabor channels.…”

Section: Existing Techniquesmentioning

confidence: 99%

Neural Network Based Hurdle Avoidance System for Smart Vehicles

Shankar¹

2019

IJNPME

View full text Add to dashboard Cite

The structure of a self-ruling vehicle dependent on neural sophisticated network for route in obscure condition is proposed. The vehicle is equipped with an IR sensor for obstacle separation estimation, a GPS collector for goal data and heading position, L298 H-connect for driving the engines which runs the wheels; all interfaced to a controller unit. The smaller scale controller forms the data gained from the sensor and GPS to produce robot movement through neural based network. The neural network running inside the small scale controller is a multi-layer feed-forward network with back-engendering blunder calculation. The network is prepared disconnected with tangent-sigmoid and positive direct estimate as enactment work for neurons and is executed progressively with piecewise straight guess of tangent-sigmoid capacity. The programming of the miniaturized scale controller is finished by PIC C Compiler and the neural network is actualized utilizing MATLAB programming. Results have shown that up to twenty neurons can be actualized in shrouded layer with this method. The vehicle is tried with differing goal places in open air situations containing fixed as well as moving obstructions and is found to arrive at the set targets effectively and its yield exactness is about equivalent to that of the normal precision.

show abstract

Impact of Automated Vehicle Eco-Approach on Human-Driven Vehicles

Cited by 20 publications

References 19 publications

Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation

Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation

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

Neural Network Based Hurdle Avoidance System for Smart Vehicles

Contact Info

Product

Resources

About