Ecological Adaptive Cruise Control With Optimal Lane Selection in Connected Vehicle Environments

Tajeddin, Sadegh; Ekhtiari, Sanaz; Faieghi, Mohammadreza; Azad, Nasser L.

doi:10.1109/tits.2019.2938726

Cited by 26 publications

(23 citation statements)

References 45 publications

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“…This study proposes a versatile, real-time implementable traffic participants behaviour predictor and situational awareness strategy for roundabout driving. The results of this study can be integrated with model predictive controllers 155-8 similar to the ones in [12] and [13] to improve performance and achieve autonomy in specific driving scenarios where expert data is available, and the general layout of the environment is known. Although there is no guarantee that the expert-yielded topology for the DBN is the optimal one, the one proposed in this specific use-case proved to be accurate and produced valid results for different scenarios, however, to achieve a well-rounded package that can produce accurate predictions for any roundabout, two aspects need to be improved.…”

Section: Discussionmentioning

confidence: 99%

Roundabout Situational Awareness for Automated Vehicles with Hybrid Machine Learning Approach

Abnili¹,

Azad²

2020

International Conference of Control, Dynamic Systems, and Robotics

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In this paper, a hybrid approach for situational awareness in roundabouts is presented that can produce traffic participants' behaviour for arbitrary horizons. This real-time implementable strategy consists of dynamic Bayesian network and a continuous variable prediction module (CVPM) as its subparts, making it a data-driven approach while providing the facility to incorporate experts' knowledge into the predictions. Being a data-driven approach, the data is obtained using SUMO as a simulation platform, and three different CVPMs are experimented with, namely recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory networks (LSTM). The chosen RNN yields a correlation higher than 0.895 and RMSE less than 0.036 for 10 seconds predictions.

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Section: Discussionmentioning

confidence: 99%

Roundabout Situational Awareness for Automated Vehicles with Hybrid Machine Learning Approach

Abnili¹,

Azad²

2020

International Conference of Control, Dynamic Systems, and Robotics

Self Cite

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“…It makes sure that the vehicle's time gap is always greater than the minimum time gap. Unlike previous studies which used soft safety constraints (i.e., adding safety penalties to the objective function) [10,29,33], this study uses a hard constraint to make sure a safe driving condition is always maintained. A soft constraint could be relaxed by accepting a penalty, but hard constraints are not open for negotiation.…”

Section: Optimization Functionmentioning

confidence: 99%

“…These ecological-ACC algorithms could maintain a safe headway while driving fuel efficiently. The studies reported up to 27% fuel saving, depending on road slope and traffic conditions [24,29,33].…”

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confidence: 99%

Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control

Vasebi

Hayeri

2021

Sustainability

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The transportation sector is the largest producer of greenhouse gas (GHG) emissions in the United States. Energy-optimal algorithms are proposed to reduce the transportation sector’s fuel consumption and emissions. These algorithms optimize vehicles’ speed to lower energy consumption and emissions. However, recent studies argued that these algorithms could negatively impact traffic flow, create traffic congestions, and increase fuel consumption on the network-level. To overcome this problem, we propose a collective-energy-optimal adaptive cruise control (collective-ACC). Collective-ACC reduces fuel consumption and emissions by directly optimizing vehicles’ trajectories and indirectly by improving traffic flow. Collective-ACC is a bi-objective non-linear integer optimization. This optimization was solved by the Non-dominated Sorting Genetic Algorithm (NSGA-II). Collective-ACC was compared with manual driving and self-centered adaptive cruise control (i.e., conventional energy-optimal adaptive cruise controls (self-centered-ACC)) in a traffic simulation. We found that collective-ACC reduced fuel consumption by up to 49% and 42% compared with manual driving and self-centered-ACC, respectively. Collective-ACC also lowered CO2, CO, NOX, and PMX by up to 54%, 70%, 58%, and 64% from manual driving, respectively. Game theory analyses were conducted to investigate how adopting collective-ACC could impact automakers, consumers, and government agencies. We propose policy and business recommendations to accelerate adopting collective-ACC and maximize its environmental benefits.

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“…In such situations it also automatically and continuously reduces the speed of the vehicle. [17,18] Fig. 1 ACC after activation.…”

Section: Adaptive Cruise Control Accmentioning

confidence: 99%

Influence of Cruise Control Use on Vehicle´s Consumption

Loman

Šarkan

Skrúcaný

2021

LOGI – Scientific Journal on Transport and Logistics

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Most vehicles used in road transport are powered by internal combustion engines. Depending on the nature of their operation, each vehicle releases a certain amount of emissions into the air. Among other things, emissions include carbon dioxide, which significantly contributes to the greenhouse effect. The quantity of CO2 produced is closely related to fuel consumption. The article presents the results of test drives which were performed on a passenger car meeting emission standard Euro 4. The aim of the study was to verify the impact of the use of cruise control on fuel consumption of the vehicle. The measurement was performed on the basis of test drives carried out on a highway section. The consumption was determined based on the data obtained from a diagnostic device, which was connected to the vehicle via OBD. The test drives were repeated several times in order to get as close as possible to the actual operation of the vehicle.

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Ecological Adaptive Cruise Control With Optimal Lane Selection in Connected Vehicle Environments

Cited by 26 publications

References 45 publications

Roundabout Situational Awareness for Automated Vehicles with Hybrid Machine Learning Approach

Roundabout Situational Awareness for Automated Vehicles with Hybrid Machine Learning Approach

Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control

Influence of Cruise Control Use on Vehicle´s Consumption

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