A fuel economic model predictive control strategy for a group of connected vehicles in urban roads

HomChaudhuri, Baisravan; Vahidi, Ardalan; Pisu, Pierluigi

doi:10.1109/acc.2015.7171149

Cited by 64 publications

(40 citation statements)

References 46 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the diversity of driving preferences among different drivers, the accurate evaluation of fuel consumption is a challenging task for intelligent vehicles, especially with plug-in hybrid electric vehicles [22]. To predict fuel use more precisely, various personalized vehicle energy consumption prediction approaches are proposed [32,43,105,112,114,118]. Authors in [105] develop a personalized multi-modality sensing and analysis system, which can efficiently extract information of user-specific driving behaviors and a hybrid electric vehicle operation profile.…”

Section: B Driving Style Recognitionmentioning

confidence: 99%

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With considering personal driving preferences and characteristics, these systems become more acceptable and trustworthy. This paper presents a survey of recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, gains of personalization, application prospects, and future focal points. Several existing driving datasets are summarized and open issues of personalized driving assistance are also suggested to facilitate future research. By creating an organized categorization of the field, this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the use of these techniques by researchers within the driving automation community.

show abstract

Section: B Driving Style Recognitionmentioning

confidence: 99%

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

show abstract

“…Security and privacy are the significant issues in VANET. Chen et al, 10 Gupte and Younis 11 Yugapriya et al, 12 Djahel et al 13 Lunge 16 Nafi et al, 17 Daniel et al, 18 Alrifaee et al 19 HomChaudhuri et al, 20 Liang and Wakahara…”

Section: V2v and V2i Applications For Road Traffic Managementmentioning

confidence: 99%

“…Current TMSs, such as in previous studies, [15][16][17][18][19][20] can predict the travel times of vehicles but not the intensity and duration of congestion. Additional data processing is required to assess the intensity and duration of congestion.…”

Section: V2v and V2i Applications For Road Traffic Managementmentioning

confidence: 99%

Characterizing the role of vehicular cloud computing in road traffic management

Ahmad

Noor

Ali

et al. 2017

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Vehicular cloud computing is envisioned to deliver services that provide traffic safety and efficiency to vehicles. Vehicular cloud computing has great potential to change the contemporary vehicular communication paradigm. Explicitly, the underutilized resources of vehicles can be shared with other vehicles to manage traffic during congestion. These resources include but are not limited to storage, computing power, and Internet connectivity. This study reviews current traffic management systems to analyze the role and significance of vehicular cloud computing in road traffic management. First, an abstraction of the vehicular cloud infrastructure in an urban scenario is presented to explore the vehicular cloud computing process. A taxonomy of vehicular clouds that defines the cloud formation, integration types, and services is presented. A taxonomy of vehicular cloud services is also provided to explore the object types involved and their positions within the vehicular cloud. A comparison of the current state-of-the-art traffic management systems is performed in terms of parameters, such as vehicular ad hoc network infrastructure, Internet dependency, cloud management, scalability, traffic flow control, and emerging services. Potential future challenges and emerging technologies, such as the Internet of vehicles and its incorporation in traffic congestion control, are also discussed. Vehicular cloud computing is envisioned to have a substantial role in the development of smart traffic management solutions and in emerging Internet of vehicles.

show abstract

“…This significantly reduces the computation time, and thus is applicable for real-time implementations. In one of our previous work [26], we have developed MPC-based fuel efficient control strategies for a group of connected vehicles in urban roads. Here, we extend that work by using F-MPC to expedite computation and enable real-time implementation, using a more stringent simulation scenario that emulates the urban driving, and by modifying the control strategy, so as to make it applicable for a wider range of driving scenarios (not only urban).…”

mentioning

confidence: 99%

Fast Model Predictive Control-Based Fuel Efficient Control Strategy for a Group of Connected Vehicles in Urban Road Conditions

HomChaudhuri

Vahidi

Pisu

2017

IEEE Trans. Contr. Syst. Technol.

Self Cite

156

View full text Add to dashboard Cite

In this paper, we develop a fast model predictive control (MPC)-based fuel economic control strategy for a group of connected vehicles in urban road conditions. The proposed control strategy is decentralized in nature, as every vehicle evaluates its own strategy using only neighborhood information. Along with the vehicle-to-vehicle communication, we exploit the signal phase and timing information from traffic lights to develop computationally efficient MPC-based strategies that reduce stopping at red lights and improve the fuel economy for a group of vehicles. The simulation results indicate the improvement in group performance and computational advantages of our proposed method.Index Terms-Connected vehicles, fast model predictive control (F-MPC), fuel economy, intelligent transportation system, optimal control.

show abstract

A fuel economic model predictive control strategy for a group of connected vehicles in urban roads

Cited by 64 publications

References 46 publications

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

Characterizing the role of vehicular cloud computing in road traffic management

Fast Model Predictive Control-Based Fuel Efficient Control Strategy for a Group of Connected Vehicles in Urban Road Conditions

Contact Info

Product

Resources

About