Journal Home > Volume 4 , Issue 3
Journal of Intelligent and Connected Vehicles 2021, 4(3): 115-124 https://doi.org/10.1108/JICV-08-2021-0012
Research paper | Open Access | Issue | Published: 18 November 2021

Ecological control strategy for cooperative autonomous vehicle in mixed traffic considering linear stability

Show Author's Information Chaoru Lu1( ),  Chenhui Liu2
Department of Civil Engineering and Energy Technology, Oslo Metropolitan University, Oslo, Norway
College of Civil Engineering, Hunan University, Changsha, China
Keywords:
Adaptive cruise control, Connected and autonomous vehicle (CAV), Linear stability
Cite this article:
Lu C, Liu C. Ecological control strategy for cooperative autonomous vehicle in mixed traffic considering linear stability. Journal of Intelligent and Connected Vehicles, 2021, 4(3): 115-124. https://doi.org/10.1108/JICV-08-2021-0012
Download citation
EndNote(RIS)
BibTeX

617

Views

10

Downloads

Citations

22

Crossref

N/A

WoS

17

Scopus

N/A

CSCD

Abstract Full text About this article
Purpose

This paper aims to present a cooperative adaptive cruise control, called stable smart driving model (SSDM), for connected and autonomous vehicles (CAVs) in mixed traffic streams with human-driven vehicles.

Design/methodology/approach

Considering the linear stability, SSDM is able to provide smooth deceleration and acceleration in the vehicle platoons with or without cut-in. Besides, the calibrated Virginia tech microscopic energy and emission model is applied in this study to investigate the impact of CAVs on the fuel consumption of the vehicle platoon and traffic flows. Under the cut-in condition, the SSDM outperforms ecological SDM and SDM in terms of stability considering different desired time headways. Moreover, single-lane vehicle dynamics are simulated for human-driven vehicles and CAVs.

Findings

The result shows that CAVs can reduce platoon-level fuel consumption. SSDM can save the platoon-level fuel consumption up to 15%, outperforming other existing control strategies. Considering the single-lane highway with merging, the higher market penetration of SSDM-equipped CAVs leads to less fuel consumption.

Originality/value

The proposed rule-based control method considered linear stability to generate smoother deceleration and acceleration curves. The research results can help to develop environmental-friendly control strategies and lay the foundation for the new methods.


menu
Abstract
Full text
Outline
About this article

Ecological control strategy for cooperative autonomous vehicle in mixed traffic considering linear stability

Show Author's information Chaoru Lu1( ), Chenhui Liu2
Department of Civil Engineering and Energy Technology, Oslo Metropolitan University, Oslo, Norway
College of Civil Engineering, Hunan University, Changsha, China

Abstract

Purpose

This paper aims to present a cooperative adaptive cruise control, called stable smart driving model (SSDM), for connected and autonomous vehicles (CAVs) in mixed traffic streams with human-driven vehicles.

Design/methodology/approach

Considering the linear stability, SSDM is able to provide smooth deceleration and acceleration in the vehicle platoons with or without cut-in. Besides, the calibrated Virginia tech microscopic energy and emission model is applied in this study to investigate the impact of CAVs on the fuel consumption of the vehicle platoon and traffic flows. Under the cut-in condition, the SSDM outperforms ecological SDM and SDM in terms of stability considering different desired time headways. Moreover, single-lane vehicle dynamics are simulated for human-driven vehicles and CAVs.

Findings

The result shows that CAVs can reduce platoon-level fuel consumption. SSDM can save the platoon-level fuel consumption up to 15%, outperforming other existing control strategies. Considering the single-lane highway with merging, the higher market penetration of SSDM-equipped CAVs leads to less fuel consumption.

Originality/value

The proposed rule-based control method considered linear stability to generate smoother deceleration and acceleration curves. The research results can help to develop environmental-friendly control strategies and lay the foundation for the new methods.

Keywords: Adaptive cruise control, Connected and autonomous vehicle (CAV), Linear stability

References(59)

Ahn, K., Rakha, H., Asce, M., Trani, A., Asce, M., Aerde, M. and Van Aerde, M. (2002), "Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels", Journal of Transportation Engineering, Vol. 128 No. 2, pp. 182-190.
DOI Google Scholar
Anesiadou, A., Makridis, M., Mattas, K., Fontaras, G. and Ciuffo, B. (2021), "Characterization of drivers heterogeneity and its integration within traffic simulation", ArXiv Preprint ArXiv: 2107.02618, available at: http://arxiv.org/abs/2107.02618

Bando, M., Hasebe, K., Nakayama, A., Shibata, A. and Sugiyarna, Y. (1995), "Dynamical model of traffic congestion and numerical simulation", Physical Review E, Vol. 51 No. 2, pp. 1035-1042.
DOI Google Scholar
Chen, X., Li, R., Xie, W. and Shi, Q. (2009), "Stabilization of traffic flow based on multi-anticipative intelligent driver model", The 12th International IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, St. Louis, pp. 72-77.

Chen, X. (M. )., Li, Z., Li, L. and Shi, Q. (2013), "Characterising scattering features in flow–density plots using a stochastic platoon model", Transportmetrica A: Transport Science, No. September, pp. 1-29.
Google Scholar

Davis, L.C. (2004), "Effect of adaptive cruise control systems on traffic flow", Physical Review E, Vol. 69 No. 6, pp. 1-8.
Google Scholar

Davis, L.C. (2007), "Effect of adaptive cruise control systems on mixed traffic flow near an on-ramp", Physica A: Statistical Mechanics and Its Applications, Vol. 379 No. 1, pp. 274-290.
DOI Google Scholar

Davis, L.C. (2017), "Dynamic origin-to-destination routing of wirelessly connected, autonomous vehicles on a congested network", Physica A: Statistical Mechanics and Its Applications, Vol. 478, pp. 93-102.
DOI Google Scholar

Desjardins, C. and Chaib-Draa, B. (2011), "Cooperative adaptive cruise control: a reinforcement learning approach", IEEE Transactions on Intelligent Transportation Systems, Vol. 12 No. 4, pp. 1248-1260.
DOI Google Scholar

Enang, W. and Bannister, C. (2017), "Modelling and control of hybrid electric vehicles (a comprehensive review)", Renewable and Sustainable Energy Reviews, Vol. 74 No. August 2016, pp. 1210-1239.
Google Scholar

Fazi, M.B. (2020), "Beyond human: deep learning, explainability and representation", Theory, Culture and Society, doi: 10.1177/0263276420966386.
DOI Google Scholar

Gipps, P.G. (1981), "A behavioural car-following model for computer simulation", Transportation Research Part B: Methodological, Vol. 15 No. 2, pp. 105-111.
DOI Google Scholar

Gunter, G., Gloudemans, D., Stern, R.E., McQuade, S., Bhadani, R., Bunting, M., Delle Monache, M.L., et al. (2019), "Are commercially implemented adaptive cruise control systems string stable?", Ieee Transactions on Intelligent Transportation Systems, pp. 1-12.
Google Scholar

Gunter, G., Janssen, C., Barbour, W., Stern, R.E. and Work, D.B. (2020), "Model based string stability of adaptive cruise control systems using field data", IEEE Transactions on Intelligent Vehicles, Vol. 5 No. 1, pp. 90-99.
DOI Google Scholar

He, D., He, W. and Song, X. (2020), "Efficient predictive cruise control of autonomous vehicles with improving ride comfort and safety", Measurement and Control, Vol. 53 Nos 1/2, pp. 18-28.
Google Scholar

Huang, K., Yang, X., Lu, Y., Mi, C.C. and Kondlapudi, P. (2018), "Ecological driving system for connected/automated vehicles using a two-stage control hierarchy", IEEE Transactions on Intelligent Transportation Systems, Vol. 19 No. 7, pp. 2373-2384.
DOI Google Scholar

Huang, L., Zhai, C., Wang, H., Zhang, R., Qiu, Z. and Wu, J. (2020), "Cooperative adaptive cruise control and exhaust emission evaluation under heterogeneous connected vehicle network environment in urban city", Journal of Environmental Management, Vol. 256 No. September 2018, p. 109975.
Google Scholar

Ioannou, P.A. and Stefanovic, M. (2005), "Evaluation of ACC vehicles in mixed traffic: lane change effects and sensitivity analysis", IEEE Transactions on Intelligent Transportation Systems, Vol. 6 No. 1, pp. 79-89.
DOI Google Scholar

Jiang, R., Wu, Q. and Zhu, Z. (2001), "Full velocity difference model for a car-following theory", Physical Review E, Vol. 64 No. 1, p. 017101.
DOI Google Scholar
Jiao, S., Zhang, S., Zhou, B., Zhang, L. and Xue, L. (2021), "Dynamic performance and safety analysis of car-following models considering collision sensitivity", Physica A: Statistical Mechanics and Its Applications, Elsevier B.V., Vol. 564, p. 125504.

Kamal, M.A.S., Mukai, M., Murata, J. and Kawabe, T. (2011), "Ecological vehicle control on roads with up-down slopes", IEEE Transactions on Intelligent Transportation Systems, Vol. 12 No. 3, pp. 783-794.
DOI Google Scholar

Kazemi, H., Mahjoub, H.N., Tahmasbi-Sarvestani, A. and Fallah, Y.P. (2018), "A learning-based stochastic MPC design for cooperative adaptive cruise control to handle interfering vehicles", IEEE Transactions on Intelligent Vehicles, Vol. 3 No. 3, pp. 266-275.
DOI Google Scholar

Kesting, A., Treiber, M. and Helbing, D. (2010), "Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity", Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 368 No. 1928, pp. 4585-4605.
DOI Google Scholar

Kesting, A., Treiber, M., Schönhof, M. and Helbing, D. (2008), "Adaptive cruise control design for active congestion avoidance", Transportation Research Part C: Emerging Technologies, Vol. 16 No. 6, pp. 668-683.
DOI Google Scholar

Kuutti, S., Bowden, R., Jin, Y., Barber, P. and Fallah, S. (2021), "A survey of deep learning applications to autonomous vehicle control", IEEE Transactions on Intelligent Transportation Systems, Vol. 22 No. 2, pp. 712-733.
DOI Google Scholar

Lenz, H., Wagner, C.K. and Sollacher, R. (1999), "Multi-anticipative car-following model", The European Physical Journal B, Vol. 7 No. 2, pp. 331-335.
DOI Google Scholar

Li, G. and Görges, D. (2020), "Ecological adaptive cruise control for vehicles with Step-Gear transmission based on reinforcement learning", IEEE Transactions on Intelligent Transportation Systems, Vol. 21 No. 11, pp. 4895-4905.
DOI Google Scholar

Li, S.E., Jia, Z., Li, K. and Cheng, B. (2015a), "Fast online computation of a model predictive controller and its application to fuel economy oriented adaptive cruise control", IEEE Transactions on Intelligent Transportation Systems, Vol. 16 No. 3, pp. 1199-1209.
DOI Google Scholar

Li, Z., Li, W., Xu, S. and Qian, Y. (2015b), "Stability analysis of an extended intelligent driver model and its simulations under open boundary condition", Physica A: Statistical Mechanics and Its Applications, Vol. 419, pp. 526-536.
DOI Google Scholar

Lin, Y., McPhee, J. and Azad, N.L. (2021), "Comparison of deep reinforcement learning and model predictive control for adaptive cruise control", IEEE Transactions on Intelligent Vehicles, Vol. 6 No. 2, pp. 221-231.
DOI Google Scholar

Lu, C. and Aakre, A. (2018), "A new adaptive cruise control strategy and its stabilization effect on traffic flow", European Transport Research Review, Vol. 10 No. 2, doi: 10.1186/s12544-018-0321-9.
DOI Google Scholar

Lu, C., Dong, J. and Hu, L. (2019a), "Energy-Efficient adaptive cruise control for electric connected and autonomous vehicles", IEEE Intelligent Transportation Systems Magazine, Vol. 11 No. 3, pp. 42-55.
DOI Google Scholar

Lu, C., Dong, J., Hu, L. and Liu, C. (2019b), "An ecological adaptive cruise control for mixed traffic and its stabilization effect", IEEE Access, Vol. 7, pp. 81246-81256.
DOI Google Scholar
Lu, C. Hu, L. and Dong, J. (2018), "Ecological adaptive cruise control in a traffic stream with mixed autonomous and manually driven vehicles".

Luo, Y., Chen, T., Zhang, S. and Li, K. (2015), "Intelligent hybrid electric vehicle ACC with coordinated control of tracking ability, fuel economy, and ride comfort", IEEE Transactions on Intelligent Transportation Systems, Vol. 16 No. 4.
Google Scholar

Mahdinia, I., Arvin, R., Khattak, A.J. and Ghiasi, A. (2020), "Safety, energy, and emissions impacts of adaptive cruise control and cooperative adaptive cruise control", Transportation Research Record: Journal of the Transportation Research Board, Vol. 2674 No. 6, pp. 253-267.
DOI Google Scholar

Makridis, M., Mattas, K., Ciuffo, B., Re, F., Kriston, A., Minarini, F. and Rognelund, G. (2020), "Empirical study on the properties of adaptive cruise control systems and their impact on traffic flow and string stability", Transportation Research Record: Journal of the Transportation Research Board, Vol. 2674 No. 4, pp. 471-484.
DOI Google Scholar

Milanés, V. and Shladover, S.E. (2016), "Handling cut-in vehicles in strings of cooperative adaptive cruise control vehicles", Journal of Intelligent Transportation Systems, Vol. 20 No. 2, pp. 178-191.
DOI Google Scholar

Newell, G.F.F. (1961), "Nonlinear effects in the dynamics of car Following", Operations Research, Vol. 9 No. 2, pp. 209-229.
DOI Google Scholar

Nunen, E., Van, Reinders, J., Semsar-Kazerooni, E. Wouw, N. and Van De, (2019), "String stable model predictive cooperative adaptive cruise control for heterogeneous platoons", IEEE Transactions on Intelligent Vehicles, Vol. 4 No. 2, pp. 186-196.
DOI Google Scholar

Pan, C., Xu, J. and Fu, J. (2021), "Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system (ADAS) evaluation", Journal of Intelligent and Connected Vehicles, pp. 28-37.
Google Scholar

Pei, X., Pan, Y., Wang, H., Wong, S.C. and Choi, K. (2016), "Empirical evidence and stability analysis of the linear car-following model with gamma-distributed memory effect", Physica A: Statistical Mechanics and Its Applications, Vol. 449, pp. 311-323.
DOI Google Scholar

Rakha, H. and Ahn, K. (2004), "Integration modeling framework for estimating mobile source emissions", Journal of Transportation Engineering, Vol. 130 No. 2, pp. 183-193.
DOI Google Scholar

Sakhdari, B. and Azad, N.L. (2018), "Adaptive tube-based nonlinear MPC for ecological autonomous cruise control of plug-in hybrid electric vehicles", IEEE Transactions on Vehicular Technology, Vol. 67 No. 12, pp. 11390-11401.
DOI Google Scholar

Shang, M. and Stern, R.E. (2021), "Impacts of commercially available adaptive cruise control vehicles on highway stability and throughput", Transportation Research Part C: Emerging Technologies, Vol. 122 No. June 2020, p. 102897.
Google Scholar

Shi, X., Wang, Z., Li, X. and Pei, M. (2021), "The effect of ride experience on changing opinions toward autonomous vehicle safety", Communications in Transportation Research, Vol. 1 No. July, p. 100003.
Google Scholar

Sun, D., Chen, D., Zhao, M., Liu, W. and Zheng, L. (2018), "Linear stability and nonlinear analyses of traffic waves for the general nonlinear car-following model with multi-time delays", Physica A: Statistical Mechanics and Its Applications, Vol. 501, pp. 293-307.
DOI Google Scholar

Tajeddin, S., Ekhtiari, S., Faieghi, M. and Azad, N.L. (2020), "Ecological adaptive cruise control with optimal lane selection in connected vehicle environments", IEEE Transactions on Intelligent Transportation Systems, Vol. 21 No. 11, pp. 4538-4549.
DOI Google Scholar

Talebpour, A. and Mahmassani, H.S. (2016), "Influence of connected and autonomous vehicles on traffic flow stability and throughput", Transportation Research Part C: Emerging Technologies, Vol. 71 No. October, pp. 143-163.
Google Scholar
Tampere, C.M.J. (2004), "Human-Kinetic multiclass traffic flow theory and modelling with application to advanced driver assistance systems in congestion", Thesis.

Wang, C., Gong, S., Zhou, A., Li, T. and Peeta, S. (2020), "Cooperative adaptive cruise control for connected autonomous vehicles by factoring communication-related constraints", Transportation Research Part C: Emerging Technologies, Vol. 113 No. March 2019, pp. 124-145.
Google Scholar

Wang, M., Hoogendoorn, S.P., Daamen, W., van Arem, B., Shyrokau, B. and Happee, R. (2018), "Delay-compensating strategy to enhance string stability of autonomous vehicle platoons", Transportmetrica B: Transport Dynamics, Vol. 6 No. 3, pp. 211-229.
DOI Google Scholar

Wilson, R.E. and Ward, J.A. (2011), "Car-following models: fifty years of linear stability analysis – a mathematical perspective", Transportation Planning and Technology, Vol. 34 No. 1, pp. 3-18.
DOI Google Scholar

Xue, W., Zheng, R., Yang, B., Wang, Z., Kaizuka, T. and Nakano, K. (2019), "An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme", Journal of Intelligent and Connected Vehicles, Vol. 2 No. 2, pp. 67-77.
DOI Google Scholar

Yang, Y., Ma, F., Wang, J., Zhu, S., Gelbal, S.Y., Kavas-Torris, O., Aksun-Guvenc, B., et al. (2020), "Cooperative ecological cruising using hierarchical control strategy with optimal sustainable performance for connected automated vehicles on varying road conditions", Journal of Cleaner Production, Vol. 275, p. 123056.
DOI Google Scholar

Yuan, Y., Jiang, R., Hu, M., Wu, Q. and Wang, R. (2009), "Traffic flow characteristics in a mixed traffic system consisting of ACC vehicles and manual vehicles: a hybrid modelling approach", Physica A: Statistical Mechanics and Its Applications, Vol. 388 No. 12, pp. 2483-2491.
DOI Google Scholar

Zhao, X., Wang, Z., Xu, Z., Wang, Y., Li, X. and Qu, X. (2020), "Field experiments on longitudinal characteristics of human driver behavior following an autonomous vehicle", Transportation Research Part C: Emerging Technologies, Vol. 114 No. February, pp. 205-224.
Google Scholar

Zheng, Z. (2021), "Reasons, challenges, and some tools for doing reproducible transportation research", Communications in Transportation Research, Vol. 1 No. July, p. 100004.
Google Scholar

Zhu, Y., He, H. and Zhao, D. (2020), "LMI-Based synthesis of String-Stable controller for cooperative adaptive cruise control", IEEE Transactions on Intelligent Transportation Systems, Vol. 21 No. 11, pp. 4516-4525.
DOI Google Scholar
Publication history
Copyright
Rights and permissions

Publication history

Received: 14 August 2021
Revised: 15 October 2021
Accepted: 19 October 2021
Published: 18 November 2021
Issue date: December 2021

Copyright

© 2021 Chaoru Lu and Chenhui Liu. Published in Journal of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited.

Rights and permissions

This articleis published under the Creative Commons Attribution (CC BY 4.0)licence. Anyone may reproduce, distribute, translate and create derivativeworks of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The fullterms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

Return