Real-Time Optimization of Fuel-Consumption and Travel-Time of CAVs for Cooperative Intersection Crossing

Hadjigeorgiou, Andreas; Timotheou, Stelios

doi:10.1109/tiv.2022.3158887

Cited by 20 publications

(8 citation statements)

References 37 publications

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“…This safe pattern is still conservative because it excludes the chances that conflicting types of vehicles coexist in an intersection. Malikopoulos et al [28], Fayazi and Vahidi [29], Cai et al [30], Wu et al [31], Lee and Park [32], and Hadjigeorgiou and Timotheou [33] employed similar strategies to model collision avoidance constraints. Levin and Rey [34] believed that intervehicle collisions occur only at finite conflicting points.…”

Section: Related Workmentioning

confidence: 99%

Sharing Traffic Priorities via Cyber–Physical–Social Intelligence: A Lane-Free Autonomous Intersection Management Method in Metaverse

Cao

Tang

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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Replacing traffic signals with roadside vehicle-toinfrastructure systems in the era of connected and autonomous vehicles (CAVs) is promising. Managing CAVs in a signal-free intersection, known as autonomous intersection management (AIM), controls the driving behavior of each intersection-traverse CAV to maximize the throughput. Although AIM improves the gross throughput, the fairness of each individual vehicle in its right of way is not seriously considered. This study sets up an AIM system in the cyber-physical-social space to trade traverse priorities quantitatively and fairly. To that end, one needs an AIM method that is optimal and stable, otherwise no convincing trades of traverse priorities could be made. This study proposes a near-optimal lane-free AIM method based on numerical optimal control, wherein log-exp functions are deployed to convexify nondifferentiable collision-avoidance constraints. Besides that, a parameterized social force model (SFM) is proposed to provide a tunable initial guess for numerical optimal control. By tuning the urgency weights in SFM, one may get cooperative trajectories in different homotopy classes, which are further utilized to decide the amount of virtual currency to reward those CAVs who tend to share their traverse priorities. The overall method improves the traverse throughput with individual fairness Manuscript

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Section: Related Workmentioning

confidence: 99%

Sharing Traffic Priorities via Cyber–Physical–Social Intelligence: A Lane-Free Autonomous Intersection Management Method in Metaverse

Cao

Tang

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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“…where x(0) is the initial condition defined in (23e), and A, B u , and B c are stacked coefficient matrices of A, B u , and B c , respectively. They are readily obtained from iterating the dynamics in ( 19) from k = 0 to k = N. By repeating the recursive steps in (21) and substituting recursive steps in (19) to eliminate the x(k) terms, the stacked coefficient matrices C f , D f u , and D f c are obtained, and hence the corresponding condensed form of the signal response function of constraint ( 21) can be written after substituting the stacked vectors defined in (28) as…”

Section: B Nominal Mpc Benchmarkmentioning

confidence: 99%

“…where C z , D zu , and D zc are stacked coefficient matrices after iterating the z(k) equation in (26) and substituting the x(k) equation in (19). Hence, the stacked formulation of the objective function (25) over a prediction horizon N can be derived by J with…”

Section: B Nominal Mpc Benchmarkmentioning

confidence: 99%

“…In the powertrain modelling aspect, a commonly used energy consumption model is the L 2 -norm of the acceleration (control input). Nevertheless, this simplified model cannot accurately predict energy usage due to the ignorance of the powertrain characteristics [18], [19]. Alternatively, a battery electric powertrain model is usually taken into account by a quadratic model of the driving force and the velocity, which strikes a balance between modelling accuracy and convexity of the problem [20], [21].…”

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confidence: 99%

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A Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles

Pan

Georgiou

et al. 2023

2023 IEEE International Conference on Mechatronics (ICM)

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This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is approached by a novel robust model predictive control (RMPC) method handling various disturbances due to modelling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semi-definite programming relaxation and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed real-time robust ecological-adaptive cruise control (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fitted convex powertrain model can be integrated into the space-domain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm.

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“…signal sequence, intersection leg index, vehicle index) or continuous variables (e.g. location, speed and acceleration rate), and thus the objectives and constraints can often be continently linearized (Chowdhury et al , 2021; Hadjigeorgiou and Timotheou, 2022).…”

Section: Joint Intersection and Connected And Automated Vehicle Controlmentioning

confidence: 99%

Intersection control with connected and automated vehicles: a review

2022

JICV

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Purpose This paper aims to review the studies on intersection control with connected and automated vehicles (CAVs). Design/methodology/approach The most seminal and recent research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control. Findings It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies. Originality/value In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions.

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Real-Time Optimization of Fuel-Consumption and Travel-Time of CAVs for Cooperative Intersection Crossing

Cited by 20 publications

References 37 publications

Sharing Traffic Priorities via Cyber–Physical–Social Intelligence: A Lane-Free Autonomous Intersection Management Method in Metaverse

Sharing Traffic Priorities via Cyber–Physical–Social Intelligence: A Lane-Free Autonomous Intersection Management Method in Metaverse

A Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles

Intersection control with connected and automated vehicles: a review

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