Green Light Optimal Speed Advisory System Designed for Electric Vehicles Considering Queuing Effect and Driver’s Speed Tracking Error

Zhang, Zhaolong; Zou, Yuan; Zhang, Xudong; Zhang, Tao

doi:10.1109/access.2020.3037105

Cited by 25 publications

(12 citation statements)

References 35 publications

(40 reference statements)

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“…On the other hand, optimal speed advisory algorithms, that help CVs navigate through a signalized corridor efficiently in terms of reduced stopped delay, fuel consumption, and CO2 emission, have been studied extensively in the literature. Many studies referred to this type of algorithm as the Green Light Optimal Speed Advisory (GLOSA) algorithm [15][16][17][18][19][20]. For instance, Suzuki and Marumo [18] developed a GLOSA system that projects a green rectangle on the roadway through the head-up display of a GLOSA-enabled vehicle.…”

Section: Related Workmentioning

confidence: 99%

Serverless Cloud-based Speed Advisory Application for Connected Vehicles – Case Study

Deng

Salek

Rahman

et al. 2022

Preprint

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In this study, we develop a real-time connected vehicle (CV) speed advisory application, which we refer to as “Serverless CloSA”, using commercial cloud services and present case studies for a signalized corridor for different roadway traffic conditions. First, we develop a highly scalable serverless cloud computing architecture using Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we develop an optimization-based real-time CV speed advisory algorithm that is deployable in the cloud. Third, we develop a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called Simulation of Urban Mobility (SUMO). Then, we conduct three case studies for three different roadway traffic conditions, i.e., low, medium, and high-density traffic. Our analyses show that Serverless CloSA can reduce the average stopped delays at signalized intersections in a corridor by 77% while reducing the aggregated risk of collision by 21% compared to the baseline scenario, i.e., no speed advisory for the CVs. Our experiments show an average end-to-end delay of 452 ms, which is well under the 1000 ms delay threshold of real-time CV mobility applications. Thus, this study also demonstrates the feasibility of deploying a real-time CV mobility application using commercial cloud services.

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Section: Related Workmentioning

confidence: 99%

Serverless Cloud-based Speed Advisory Application for Connected Vehicles – Case Study

Deng

Salek

Rahman

et al. 2022

Preprint

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“…This may cause sharp accelerations or hard braking, and unnecessary idling time, which, in turn, cause additional fuel consumption and emissions ( 12 , 13 ). Therefore, a great deal of research efforts have focused on optimizing such driving behavior using CV technology ( 8–10 , 12 , 14 , 15 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…There exists a large body of literature on SAS modeling and its impact analysis. However, the primary focus of the literature is placed only on traffic delay, fuel consumption, and emission savings ( 1 , 7–10 ). For practical applications, several field-based experiments have been conducted in restricted road environments, yet their focus is also limited to energy and emission savings ( 6 , 11 , 12 ).…”

mentioning

confidence: 99%

Safety Benefits of Automated Speed Advisory Systems at Signalized Intersections

Saunier

Miranda-Moreno

2022

Transportation Research Record: Journal of the Transportation R

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Human-driving behavior at signalized intersections may lack efficiency because drivers try to reach their desired speed without the upcoming traffic-signal information. This causes idling time, sharp accelerations, hard braking, traffic congestion, emissions, and energy consumption. Connected vehicles, for example those equipped with a speed advisory system (SAS), can provide prior information to drivers for optimizing their driving behavior while approaching signalized intersections. However, the current literature focuses only on the fuel consumption, emissions, and travel-delay reduction impacts of SASs. This paper evaluates the safety impact of SAS vehicles using the proposed approach that simulates mixed-traffic situations between SAS and human-driven vehicles (HDVs). HDVs in the model follow real vehicle trajectories based on car-following conditions. The study investigates various scenarios including the impact of the different ranks of SAS vehicles in the vehicle group, the lane-changing possibility, and market penetration rates (MPRs). The results suggest that SAS vehicles can reduce rear-end collision risks from 25% MPR. The minimum time to collision increases by 1.2 s and the deceleration rate to avoid crash declines by 0.3 [Formula: see text] on average for 100% MPR relative to 0%. The study demonstrated that this safety benefit is also strongly related to the rank of SAS vehicles within a vehicle group. In addition, the conflict locations in the approaching lane gradually move away from the intersection up to where the communication range starts as the MPR increases, which would reduce abrupt vehicle speed changes near pedestrian crosswalks.

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“…Therefore, to mitigate such a gap, actual driving tests are usually performed. Zhaolong Zhang et al [150] experimentally evaluated the human error in tracking an optimized speed profile. These measures were used to model the human error, which was hence integrated in the proposed SMPC-based GLOSA system.…”

Section: Or Peer Reviewmentioning

confidence: 99%

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

et al. 2021

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Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

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Green Light Optimal Speed Advisory System Designed for Electric Vehicles Considering Queuing Effect and Driver’s Speed Tracking Error

Cited by 25 publications

References 35 publications

Serverless Cloud-based Speed Advisory Application for Connected Vehicles – Case Study

Serverless Cloud-based Speed Advisory Application for Connected Vehicles – Case Study

Safety Benefits of Automated Speed Advisory Systems at Signalized Intersections

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

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