Longitudinal collision mitigation via coordinated braking of multiple vehicles using model predictive control

This paper considers the robustness analysis and distributed H∞ (H-infinity) controller synthesis for a platoon of connected vehicles with undirected topologies. We first formulate a unified model to describe the collective behavior of homogeneous platoons with external disturbances using graph theory. By exploiting the spectral decomposition of a symmetric matrix, the collective dynamics of a platoon is equivalently decomposed into a set of subsystems sharing the same size with one single vehicle. Then, we provide an explicit scaling trend of robustness measure γ-gain, and introduce a scalable multistep procedure to synthesize a distributed H∞ controller for large-scale platoons. It is shown that communication topology, especially the leader's information, exerts great influence on both robustness performance and controller synthesis. Further, an intuitive optimization problem is formulated to optimize an undirected topology for a platoon system, and the upper and lower bounds of the objective are explicitly analyzed, which hints us that coordination of multiple mini-platoons is one reasonable architecture to control large-scale platoons. Numerical simulations are conducted to illustrate our findings.

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“…As used in [13], [14], [28], we consider the following model to describe longitudinal vehicle dynamicṡ…”

Section: Simulations With Realistic Vehicle Dynamicsmentioning

confidence: 99%

Platooning of Connected Vehicles With Undirected Topologies: Robustness Analysis and Distributed H-infinity Controller Synthesis

Zheng

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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178

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“…Behavioral characteristics, including steering wheel movement (SWM), standard deviation of the lane position (SDLP), and steering wheel angle (SWA) are excellent because they are reliable, real-time, and non-invasive, as the sensors embedded in various places inside the vehicle can acquire the operating information accurately and in real time. These characteristics have already demonstrated great importance in driving assistance systems [16,17,18]. …”

Section: Introductionmentioning

confidence: 99%

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

et al. 2017

Sensors

149

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This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn) features from fixed sliding windows on real-time steering wheel angles time series. After that, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: “wake” and “drowsy”. The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the “awake” state, and 15.15% false detections of the “drowsy” state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue.

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“…An action taken by has a safety impact on one or more of the ( − 1) other vehicles -and hence influences their actions in different ways. constructs influence relations between the intended [steer,speed] actions of every pairwise vehicles considered [16]. The influence relations factor in the vehicle parameters (e.g., speed, weight) and environmental parameters (e.g., road wetness, visibility).…”

Section: B Structure Of Multi-vehicle Collision Avoidance Systemmentioning

confidence: 99%

External Assessment of QoS Provisioning in Distributed Cloud Services

Ravindran

Adiththan

Iannelli

et al. 2016

2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W)

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Given a distributed service support system realized on a cloud (e.g., replicated data services), QoS assessment captures the SLA violations that may arise under various resource depletions and outages faced by . The issue of less-than-100% guarantee of QoS arises due to the lax control of the underlying cloud resources and components that is typical of third-party providers. Our goal is to reason about how well the system-internal mechanisms are geared to offer a required level of service to the application. We employ computational models of to determine the optimal feasible output and verify how close is the actual behavior of to this 'gold-standard'. Case studies from diverse application domains are described to corroborate our assessment methodology.

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Longitudinal collision mitigation via coordinated braking of multiple vehicles using model predictive control

Cited by 80 publications

References 41 publications

Platooning of Connected Vehicles With Undirected Topologies: Robustness Analysis and Distributed H-infinity Controller Synthesis

Platooning of Connected Vehicles With Undirected Topologies: Robustness Analysis and Distributed H-infinity Controller Synthesis

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

External Assessment of QoS Provisioning in Distributed Cloud Services

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