Gaussian Process based Stochastic Model Predictive Control for Cooperative Adaptive Cruise Control

Mosharafian, Sahand; Razzaghpour, Mahdi; Fallah, Yaser P.; Velni, Javad Mohammadpour

doi:10.1109/vnc52810.2021.9644629

Cited by 19 publications

(9 citation statements)

References 16 publications

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“…When designing CACC systems, we must explicitly account for the uncertainty in vehicle state, behavior, and communication [33]. Because the information from the neighboring vehicles is not continuously available, each agent must run an estimator between these instances, as shown in Figure 2.…”

Section: ) Model-based Communicationmentioning

confidence: 99%

Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control

Razzaghpour

Valiente

Zaman

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Utilizing Vehicle-to-everything (V2X) communication technologies, vehicle platooning systems are expected to realize a new paradigm of cooperative driving with higher levels of traffic safety and efficiency. Connected and Autonomous Vehicles (CAVs) need to have proper awareness of the traffic context. The cooperative platoon's performance will be influenced by the communication strategy. In particular, time-triggered or event-triggered are of interest here. The expenses related to communication will increase significantly as the number of connected entities increases. Periodic communication can be relaxed to more flexible aperiodic or event-triggered implementations while maintaining desired levels of performance. This paper proposes a predictive model-based and control-aware communication solution for vehicle platoons. The method uses a fully distributed Event-Triggered Communication (ETC) strategy combined with Model-Based Communication (MBC) and aims to minimize communication resource usage while preserving desired closed-loop performance characteristics. In our method, each vehicle runs a remote vehicle state estimator based on the most recently communicated model and the event-driven communication scheme only updates the model when the performance metric error exceeds a certain threshold. Our approach achieves a significant reduction in the average communication rate (82%) while only slightly reducing control performance (e.g., less than 1% speed deviation).

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Section: ) Model-based Communicationmentioning

confidence: 99%

Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control

Razzaghpour

Valiente

Zaman

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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“…This approach adjusts the model complexity to the observed data, allowing it to capture distinct patterns as they arise during training. This idea has been utilized in [49,50] to enhance the performance of Cooperative Adaptive Cruise Control (CACC) in congested traffic scenarios.…”

Section: Tracking and Predictionmentioning

confidence: 99%

Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

Valiente

Raftari

Mahjoub

et al. 2023

IET Intelligent Trans Sys

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Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors' failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems is proposed. The CA-TC consists of two modules: a Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles' path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior is learned. Offline training and online model updates are combined with on-the-fly forecasting to account for new possible driver behaviors. Finally, the framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

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“…Machine learning paradigms [37,35,4,38,34] are built upon the assumption that training and test data have the same probability distributions. When this hypothesis is even marginally broken, as most real-life settings, a significant drop in performance can be observed.…”

Section: Domain Generalization and Ensemble Modelsmentioning

confidence: 99%

Robust Ensemble Morph Detection with Domain Generalization

Kashiani¹,

Sami²,

Soleymani³

et al. 2022

Preprint

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Although a substantial amount of studies is dedicated to morph detection, most of them fail to generalize for morph faces outside of their training paradigm. Moreover, recent morph detection methods are highly vulnerable to adversarial attacks. In this paper, we intend to learn a morph detection model with high generalization to a wide range of morphing attacks and high robustness against different adversarial attacks. To this aim, we develop an ensemble of convolutional neural networks (CNNs) and Transformer models to benefit from their capabilities simultaneously. To improve the robust accuracy of the ensemble model, we employ multi-perturbation adversarial training and generate adversarial examples with high transferability for several single models. Our exhaustive evaluations demonstrate that the proposed robust ensemble model generalizes to several morphing attacks and face datasets. In addition, we validate that our robust ensemble model gain better robustness against several adversarial attacks while outperforming the state-of-the-art studies.

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Gaussian Process based Stochastic Model Predictive Control for Cooperative Adaptive Cruise Control

Cited by 19 publications

References 16 publications

Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control

Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control

Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

Robust Ensemble Morph Detection with Domain Generalization

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