Impact of Information Flow Topology on Safety of Tightly-coupled Connected and Automated Vehicle Platoons Utilizing Stochastic Control

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

doi:10.23919/ecc55457.2022.9837992

Cited by 11 publications

(12 citation statements)

References 14 publications

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“…These sensor limitations restrict ACC from functioning in gaps shorter than one second, limiting its practicality in moderate to heavy traffic where other drivers may attempt to cut into the one-second gap. CACC systems must be able to handle special maneuvers like interfering vehicles cutting into CACC platoons or leading cars hard braking [3], [14], [15]. V2V communication, with its faster and more accurate information on the preceding vehicle's motion, allows the CACC vehicle to follow the leading vehicle with greater precision and at a much shorter gap.…”

Section: A Cooperative Adaptive Cruise Control (Cacc)mentioning

confidence: 99%

“…where v 0 (t) is the leader's speed. Detecting changes in the speeds of preceding vehicles is essential for attenuating or eliminating traffic shock waves that cause "stop and go" disturbances [3].…”

Section: A Cooperative Adaptive Cruise Control (Cacc)mentioning

confidence: 99%

“…Cooperative Adaptive Cruise Control (CACC), enabled by Vehicle-to-Vehicle (V2V) communication, promises to enhance traffic flow behavior regarding string stability while achieving short inter-vehicle gaps. When compared to ACC, this makes it more powerful in terms of preventing collisions while also maximizing traffic throughput [2], [3].…”

Section: Introductionmentioning

confidence: 99%

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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: A Cooperative Adaptive Cruise Control (Cacc)mentioning

confidence: 99%

Section: A Cooperative Adaptive Cruise Control (Cacc)mentioning

confidence: 99%

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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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“…The AVs intend to offer a safe and comfortable ride using the output of sensory units, a map, and a high-level route [9][10][11]. Meanwhile, in safety-critical applications such as self-driving cars, creating interpretable intermediate representations that explain why the car performed a given maneuver is critical for decision-making [12,13].…”

Section: Introductionmentioning

confidence: 99%

High-Definition Map Representation Techniques for Automated Vehicles

Soorchaei¹,

Razzaghpour²,

Romero³

et al. 2022

Preprint

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Many studies in the field of robot navigation have focused on environment representation and localization. The goal of map representation is to summarize spatial information in topological and geometrical abstracts. By providing strong priors, maps improve the performance and reliability of automated robots. Due to the transition to fully automated driving in recent years, there has been a constant effort to design methods and technologies to improve the precision of road participants and the environment's information. Among these efforts is the High Definition (HD) Map concept. Making HD maps requires accuracy, completeness, verifiability, and extensibility. Because of the complexity of HD mapping, it is currently expensive and difficult to implement, particularly in an urban environment. In an urban traffic system, the road model is at least a map with sets of roads, lanes, and lane markers. While more research is being dedicated to mapping and localization, a comprehensive review of the various types of map representation is still required. This paper presents a brief overview of map representation, followed by a detailed literature review of HD Map for automated vehicles. The current state of AV mapping is encouraging, the field has matured to a point where detailed maps of complex environments are built in real-time and have been proved useful. Many existing techniques are robust to noise and can cope with a large range of environments. Nevertheless, there are still open problems for future research. AV mapping will continue to be a highly active research area essential to the goal of achieving full autonomy.

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Impact of Information Flow Topology on Safety of Tightly-coupled Connected and Automated Vehicle Platoons Utilizing Stochastic Control

Cited by 11 publications

References 14 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

High-Definition Map Representation Techniques for Automated Vehicles

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