Utilizing Model-Based Communication and Control for Cooperative Automated Vehicle Applications

2019 IEEE International Systems Conference (SysCon)

Toghi

Gani

et al. 2019

Self Cite

Scalable communication is of utmost importance for reliable dissemination of time-sensitive information in cooperative vehicular ad-hoc networks (VANETs), which is, in turn, an essential prerequisite for the proper operation of the critical cooperative safety applications. The model-based communication (MBC) is a recently-explored scalability solution proposed in the literature, which has shown a promising potential to reduce the channel congestion to a great extent. In this work, based on the MBC notion, a technology-agnostic hybrid model selection policy for Vehicle-to-Everything (V2X) communication is proposed which benefits from the characteristics of the non-parametric Bayesian inference techniques, specifically Gaussian Processes. The results show the effectiveness of the proposed communication architecture on both reducing the required message exchange rate and increasing the remote agent tracking precision.Index Terms-Vehicular ad-hoc network, scalable V2X communication, model-based communication, non-parametric Bayesian inference, Gaussian processes.

Section: Model-based Communication Overviewmentioning

confidence: 99%

V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures

2019 IEEE International Systems Conference (SysCon)

Toghi

Gani

et al. 2019

Self Cite

“…This section is briefly describing our overall design and its core components, i.e., the error-driven MBC strategy and GP inference, which are essential for the rest of this work. Interested readers could refer to our previous works for further information [10], [11], [12], [13], [14], [15].…”

Section: Problem Statementmentioning

confidence: 99%

Representing Realistic Human Driver Behaviors using a Finite Size Gaussian Process Kernel Bank

2019 IEEE Vehicular Networking Conference (VNC)

Raftari

Valiente

et al. 2019

Self Cite

The performance of cooperative vehicular applications is tightly dependent on the reliability of the underneath Vehicle-to-Everything (V2X) communication technology. V2X standards, such as Dedicated Short-Range Communications (DSRC) and Cellular-V2X (C-V2X), which are passing their research phase before being mandated in the US, are supposed to serve as reliable circulatory systems for the timecritical information in vehicular networks; however, they are still heavily suffering from scalability issues in real traffic scenarios. The technology-agnostic notion of Model-Based Communications (MBC) has been proposed in our previous works as a promising paradigm to address the scalability issue and its performance, while acquiring different modeling strategies, has been vastly studied. In this work, the modeling capabilities of a powerful nonparametric Bayesian inference scheme, i.e., Gaussian Processes (GPs), is investigated within the MBC context with more details. Our observations reveal an important potential strength of GPbased MBC scheme, i.e., its capability of accurately modeling different driving behavioral patterns by utilizing only a limited size GP kernel bank. This interesting aspect of integrating GP inference with MBC framework, which has been verified in this work using realistic driving data sets, introduces this architecture as a strong and appealing candidate to address the scalability challenge. The results confirm that our proposed approach overperforms the state of the art research in terms of the required communication rate and GP kernel bank size.Index Terms-Vehicular ad-hoc network, scalable V2X communication, model-based communication, non-parametric Bayesian inference, Gaussian processes, driver behavior modeling.

“…An innovative idea has been recently proposed in [18] and more investigated in [19] as the model-based communication (MBC) which proposes a new design perspective to be utilized for the DSRC congestion control problem. This methodology proposes to replace the mixed vehicle/driver behavior with an abstract description (model) and then share the models and their updates over the network instead of directly communicating raw dynamic information.…”

Section: Introductionmentioning

confidence: 99%

A Driver Behavior Modeling Structure Based on Non-Parametric Bayesian Stochastic Hybrid Architecture

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

Toghi

Fallah

2018

Self Cite

Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semiautomated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical stochastic hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a stochastic object. Nonparametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.