research-article
Authors: Anye Zhou, Srinivas Peeta, and Jian Wang
Computer‐Aided Civil and Infrastructure Engineering, Volume 38, Issue 18
Pages 2513 - 2536
Published: 14 November 2023 Publication History
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Abstract
By using kinematic state information obtained through vehicle‐to‐vehicle communications, connected autonomous vehicles (CAVs) can drive cooperatively to alleviate shockwave propagation associated with traffic disturbances. However, during the transition to full autonomy, CAVs and human‐driven vehicles (HDVs) will coexist on the road, creating mixed‐flow traffic. The inherent heterogeneity and randomness in human driving behavior can generate additional disturbances in the traffic flow. Further, HDVs without communication functionality (unconnected HDVs) can cause the control performance of CAVs to degrade by negatively impacting platoon formation. To proactively mitigate the negative impacts of HDVs in mixed‐flow traffic, this study proposes a cooperative control strategy with three components for platoons consisting of CAVs and unconnected HDVs: (i) a number estimator for estimating the number of HDVs between two CAVs, (ii) a kinematic state predictor for predicting the kinematic states of HDVs, and (iii) a multi‐anticipative car‐following controller (i.e., control strategy using kinematic state information of multiple preceding vehicles) for CAVs to maintain string stability and desired time headway. To initialize the proposed strategy, the number estimator is developed using a deep neural network (DNN). Then, a DNN‐based kinematic state predictor predicts the kinematic states of HDVs for CAVs to enable multi‐anticipative car‐following control. The multi‐anticipative car‐following controller is implemented using an extended intelligent driver model‐guided deep deterministic policy gradient algorithm, which ensures safety, string stability, and traffic efficiency. The effectiveness of the proposed control strategy is validated through numerical experiments using NGSIM data. Results indicate that the proposed strategy can produce accurate estimations of the number and the kinematic states of HDVs between CAVs. Further, it can achieve string stability while maintaining smaller time headways, compared to car‐following models used for training guidance under different market penetration rates of CAVs, which significantly improves traffic smoothness and mobility.
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- Zhang TJin PPiccoli BSartipi M(2024)Deep spatial‐temporal embedding for vehicle trajectory validation and refinementComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1316039:11(1597-1615)Online publication date: 21-May-2024
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Published In
Computer-Aided Civil and Infrastructure Engineering Volume 38, Issue 18
December 2023
180 pages
ISSN:1093-9687
EISSN:1467-8667
DOI:10.1111/mice.v38.18
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© 2023 Computer‐Aided Civil and Infrastructure Engineering.
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John Wiley & Sons, Inc.
United States
Publication History
Published: 14 November 2023
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- Grosset JFougères ADjoko-Kouam MBonnin J(2024)Multi-agent simulation of autonomous industrial vehicle fleetsIntegrated Computer-Aided Engineering10.3233/ICA-24073531:3(249-266)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.3233/ICA-240735
- Zhang TJin PPiccoli BSartipi M(2024)Deep spatial‐temporal embedding for vehicle trajectory validation and refinementComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1316039:11(1597-1615)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1111/mice.13160
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