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Wide area control for improving stability of a power system with plug-in electric vehicles

IET GENERATION TRANSMISSION & DISTRIBUTION 2010年第10期4卷 1151-1163页

作者： Mitra, P. Venayagamoorthy, G. K. Missouri Univ Sci & Technol RTPIS Lab Rolla MO 65409 USA

The integration of plug-in electric vehicles (PEVs) to power systems has impacts on the stability characteristics of the integrated systems. Wide area controllers (WACs) are used in power systems to provide auxiliary control signals to the generators or other devices in order to improve the stability of the system. The necessity of WACs becomes more relevant during grid-to-vehicle (G2V) or vehicle-to-grid (V2G) power transactions, that is, charging and discharging cycles of the PEVs respectively. The design of a WAC for providing damping to three generators in a 12-bus power system with PEVs is presented in this study. Each WAC signal is obtained based on the aggregation of modulated local and remote power system stabilisers' signals. The modulation indices associated with those signals are tuned using the particle swarm optimisation technique to provide the maximum damping to the three generators. The 12-bus power system with the PEVs and WAC has been implemented on the real-time digital simulator (RTDS). Typical results have been presented to show the improvement in the stability of the power system when PEVs are integrated using transient simulations and Prony analysis.

关键词： particle swarm optimisation electric vehicles control of electric power systems auxiliary control signals Prony analysis Optimisation techniques power system stability improvement particle swarm optimisation technique Power system control Transportation wide area controllers real-time digital simulator plug-in electric vehicles damping road-traffic system control damping control signals Stability in control theory local power system stabiliser vehicle-to-grid power transactions power system stability

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Reinforcement learning-based multi-agent system for network traffic signal control

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IET INTELLIGENT TRANSPORT systemS 2010年第2期4卷 128-135页

作者： Arel, I. Liu, C. Urbanik, T. Kohls, A. G. Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Univ Tennessee Dept Civil & Environm Engn Knoxville TN 37996 USA

A challenging application of artificial intelligence systems involves the scheduling of traffic signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi-agent system and reinforcement learning (RL) framework to obtain an efficient traffic signal control policy. The latter is aimed at minimising the average delay, congestion and likelihood of intersection cross-blocking. A five-intersection traffic network has been studied in which each intersection is governed by an autonomous intelligent agent. Two types of agents, a central agent and an outbound agent, were employed. The outbound agents schedule traffic signals by following the longest-queue-first (LQF) algorithm, which has been proved to guarantee stability and fairness, and collaborate with the central agent by providing it local traffic statistics. The central agent learns a value function driven by its local and neighbours' traffic conditions. The novel methodology proposed here utilises the Q-Learning algorithm with a feedforward neural network for value function approximation. Experimental results clearly demonstrate the advantages of multi-agent RL-based control over LQF governed isolated single-intersection control, thus paving the way for efficient distributed traffic signal control in complex settings.

关键词： traffic engineering computing Neural computing techniques multiintersection vehicular networks feedforward neural network local traffic statistics traffic signal scheduling feedforward neural nets Knowledge engineering techniques reinforcement learning-based multi-agent system longest-queue-first algorithm road-traffic system control traffic control control engineering computing learning (artificial intelligence) network traffic signal control multi-agent systems road traffic control engineering computing scheduling Q-Learning algorithm

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DIGITAL-COMPUTER control OF VEHICLES IN AN AUTOMATED TRANSPORTATION system

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PROCEEDINGS OF THE INSTITUTION OF ELECTRICAL ENGINEERS-LONDON 1973年第10期120卷 1267-1272页

作者： RUMSEY, AF POWNER, ET UNIV MANCHESTER INST SCI & TECHNOLDEPT ELECT ENGN & ELECTRPOB 88MANCHESTER M60 1QDENGLAND

In recent years considerable interest has been focused on the possibility of introducing some form of automatic vehicle control as a means of easing congestion in densely populated urban areas. Automatic control enables vehicles to operate safely at high speeds and with small headways, and hence results in increased system capacity. In this paper the authors consider how vehicles in an automated network can be controlled electronically using an on-line digital computer. A ‘cell-following’ control philosophy is assumed and the problem of controlling vehicles on a main-line guideway is modelled as a linear-state regulator problem in discrete-time form. The resulting optimal-control law is found to be stationary, independent of initial conditions and capable of yielding good vehicle-response characteristics. Finally the authors show how the system model can be extended to cater for the problem of merging vehicles from an off-line station onto the main-line guideway. In this case the optimal-control law is shown to be no longer time invariant but can again be precomputed for a given set of initial conditions.

关键词： transportation Transportation automated transportation system digital computer control optimal control road-traffic system control vehicles road vehicles control engineering applications of computers control engineering computing automatic control

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Active collision avoidance system for steering control of autonomous vehicles

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IET INTELLIGENT TRANSPORT systemS 2014年第6期8卷 550-557页

作者： Lin, Ching-Fu Juang, Jyh-Ching Li, Kun-Rui Natl Cheng Kung Univ Dept Elect Engn Tainan 701 Taiwan

The study proposes an active collision avoidance system to allow safe lane-changing manoeuvres by self-steering vehicles in the presence of the uncertainties associated with nearby vehicles and the surrounding environment. This system integrates estimation of conflict probability, model predictive control and dedicated short-range communications (DSRC) techniques to ensure a collision-free operation. To accomplish this, the proposed system uses model predictive control to predict the future positions of vehicles and estimates the conflict probability so as to reduce the risk of collision. The system also exploits DSRC techniques to facilitate the gathering of information from nearby vehicles so that potential conflicts can be detected at an earlier stage. Autonomous vehicles can thus make adjustments based on the acquired data to avoid collisions in a real communication environment. The effectiveness of the method has been verified under experimental conditions. The influences of key parameters in the control method are examined.

关键词： autonomous vehicle steering control probability Spatial variables control Optimal control data acquisition dedicated short-range communication techniques predictive control road traffic control collision risk collision-free operation active collision avoidance system conflict probability safe lane-changing manoeuvres model predictive control road-traffic system control self-steering vehicles DSRC techniques position prediction collision avoidance steering systems road safety vehicle dynamics Other topics in statistics

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Estimating the impact of abnormal loads on network performance

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IET INTELLIGENT TRANSPORT systemS 2009年第2期3卷 111-123页

作者： Taylor, N. B. Hardman, E. J. Weekley, J. Bourne, N. Transport Res Lab Wokingham RG40 3GA England

The English Highways Agency is required to improve network performance including journey time reliability measured by average delay on the 10% slowest journeys along a specified set of primary routes in the strategic road network. Abnormal indivisible loads, which behave as moving bottlenecks, can cause significant delay, but the factors involved are complex, so methods have been developed to model their effect. These methods have been extended to estimate their impact through the Average Vehicle Delay (formerly PSA1 delay) measure, allowing mitigations through intelligent transportation system or policy options to be evaluated. Environmental impacts are also considered.

关键词： average vehicle delay environmental impacts abnormal load environmental factors English Highways Agency impact estimation automated highways Natural resources and environmental control road-traffic system control intelligent transportation system road vehicles journey time reliability network performance

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Cyberattacks on connected automated vehicles: A traffic impact analysis

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IET INTELLIGENT TRANSPORT systemS 2023年第2期17卷 295-311页

作者： Sun, Zhanbo Liu, Runzhe Hu, Haitao Liu, Dengyue Yan, Zhiqi Southwest Jiaotong Univ Sch Transportat & Logis Chengdu 611756 Peoples R China Natl Engn Lab Integrated Transportat Big Data App Chengdu Peoples R China

Cybersecurity has become one of the major challenges for Connected Automated Vehicles. Previous work in this field mostly focused on the detection of and defence against Connected Automated Vehicle-related cyberattacks. Using performance measures collected from traffic micro-simulations, the study analysed the traffic impacts of cyberattacks on Connected Automated Vehicles. Two types of adversary models, namely, time-delay attacks and disturbance attacks, were applied to the simulated traffic on freeway segments and un-signalised intersections, respectively. The effects of various cyberattacks were evaluated based on safety indicators, including Time-To-Collision, Deceleration Rate to Avoid Collision, and efficiency indicators, including speed and flow-density diagrams. The results revealed that both attacks increase the risk and severity of collisions on freeway segments and un-signalised intersections. Location-based time-delay attacks will result in significant deceleration, congestion and reduction in road throughput. Disturbance attacks not only cause congestion but also result in frequent acceleration/deceleration and uneven distribution of traffic density. The impacts of attacks are more severe on heavy traffic. At un-signalised intersections, location-based disturbance attacks lead to a significantly increased risk of right-angled collisions. The results could help better understand the effects of Connected Automated Vehicle-related cyberattacks and shed light on proactive defence against such attacks.

关键词： traffic engineering computing connected automated vehicles Connected Automated Vehicle-related cyberattacks freeway segments heavy traffic traffic microsimulations location-based time-delay attacks road traffic control Time-To-Collision security of data traffic engineering computing location-based disturbance attacks road-traffic system control Data security simulated traffic intelligent transportation systems traffic impacts road traffic computer network security traffic density computer crime Computer communications road vehicles traffic impact analysis un-signalised intersections

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road prediction using video for integrated driver support

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COMPUTING & control ENGINEERING JOURNAL 1999年第4期10卷 169-175页

作者： Hermans, FJJ LucasVar Automot Adv Prod Dev Solihull B90 4GW W Midlands England

The use of a video camera for predicting vehicle trajectory is reviewed. The data available from the video camera, and the features which can be used for trajectory prediction and how these relate to the real world, are analysed. To accurately predict the road ahead, a model is used, consisting of a spatial and dynamic module. A recursive least square based estimator is designed based on the road model. The estimator is robust with respect to variations in the horizon, deals with distance and varying measurement noise, and handles the spatial/dynamic structure. The results are presented in two separate parts. The first deals with the estimation and comprises simulated data to illustrate robustness, and video data to show typical road features. The second is the performance analysis. It highlights the response to changes in road parameters and the subsequent correlation with standard road prediction techniques.

关键词： least squares approximations performance analysis simulated data spatial module road prediction automated highways video signal processing Interpolation and function approximation (numerical analysis) road vehicles road model traffic engineering computing video camera vehicle trajectory prediction spatial structure distance measurement noise robustness dynamic structure road-traffic system control recursive least squares based estimator control engineering computing dynamic module road parameter change response Video signal processing typical road features Numerical analysis integrated driver support varying measurement noise

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Higher-order sliding mode observer for estimation of tyre friction in ground vehicles

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IET control THEORY AND APPLICATIONS 2014年第6期8卷 399-408页

作者： Rath, Jagat Jyoti Veluvolu, Kalyana Chakravarthy Defoort, Michael Soh, Yeng Chai Kyungpook Natl Univ Sch Elect Engn Taegu South Korea Univ Lille Nord France CNRS UMR 8201 LAMIH UVHC F-59313 Valenciennes France Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The estimation of friction coefficient for a vehicle when it traverses on different surfaces has been an important issue. In this work, the longitudinal vehicle dynamics, the torsional tyre dynamics and the non-linear LuGre friction dynamics are integrated to model the quarter vehicle dynamics. The road adhesion coefficient in the vehicle dynamics is unknown and varies with the contact surface. To address this issue, the authors consider a class of non-linear uncertain systems that covers the vehicle dynamics and develop a higher-order sliding mode observer based on supertwisting algorithm for state and unknown input estimations. Under Lipschitz conditions for the non-linear functions, the convergence of the estimation error is established. By estimating the road adhesion coefficient, the coefficient of friction can be estimated. Simulation results demonstrate the effectiveness of the proposed observer for state and unknown input estimation.

关键词： nonlinear systems tyres tyre friction estimation friction higher-order sliding mode observer road adhesion coefficient Vehicle mechanics Multivariable control systems state input estimations Lipschitz conditions nonlinear LuGre friction dynamics automotive vehicle industry variable structure systems Nonlinear control systems state estimation unknown input estimations longitudinal vehicle dynamics vehicle dynamics torsional tyre dynamics Automobile industry Tribology (mechanical engineering) road vehicles friction coefficient estimation automobile industry road-traffic system control supertwisting algorithm quarter vehicle dynamics Mathematical analysis uncertain systems ground vehicles estimation error convergence nonlinear functions road traffic control observers Simulation, modelling and identification contact surface nonlinear uncertain systems

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Evaluation of emergency driving behaviour and vehicle collision risk in connected vehicle environment: A deep learning approach

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IET INTELLIGENT TRANSPORT systemS 2021年第4期15卷 584-594页

作者： Peng, Liqun Wang, Yiting Zhang, Fan Zhang, Jisheng Li, Zhixiong East China Jiaotong Univ Sch Transportat & Logist Nanchang Jiangxi Peoples R China Beijing GOTEC ITS Technol Co Ltd Beijing Peoples R China Yonsei Univ Yonsei Frontier Lab 50 Yonsei Ro Seoul 03722 South Korea Univ Alberta Dept Civil & Environm Engn Edmonton AB Canada

In the latest connected vehicle (CV) message standards, including SAE J2735-2016 and T-CASE 53-2017, the basic safety messages (BSMs) are designed specifically as effective measures for traffic safety management and applications. In this study, a testbed on the Nanchang-Jiujiang Intelligent Highway in Jiangxi, China is illustrated as an example, and the basic architecture and key technologies is introduced for a proactive traffic safety utilisation, where the core basic safety message (BSM) data are sorted and implemented to perceive and predict risky driving behaviours in a field environment. On this basis, an accurate insight into time-critical driving safety issues can be achieved by investigating raw BSM data, such as the inter-vehicle distance, driver manipulation, vehicle speed, and acceleration/deceleration. Furthermore, to effectively take advantage of connected vehicle information and perceive the high uncertainty of driving behaviours during an emergency situation and evaluate the driving safety in mixed traffic scenarios, a long short-term memory (LSTM) based deep learning framework is introduced to build a multi-horizon vehicle crash risk prediction model using continuous BSMs as the inputs. The experimental results demonstrate the significance of connected vehicle data and deep learning algorithms for improving driving safety and promoting widespread deployment and application of connected vehicles.

关键词： traffic engineering computing deep learning approach vehicle speed road safety connected vehicle data latest connected vehicle connected vehicles road traffic control time-critical driving safety issues message standards effective measures roads deep learning algorithms emergency situation promoting widespread deployment raw BSM data field environment proactive traffic safety utilisation T-CASE 53-2017 SAE J2735-2016 deep learning framework risky driving behaviours connected vehicle information basic safety messages road-traffic system control Nanchang-Jiujiang Intelligent Highway core basic safety message data road vehicles connected vehicle environment mixed traffic scenarios multihorizon vehicle crash risk prediction model learning (artificial intelligence) inter-vehicle distance vehicle collision risk road traffic traffic safety management Other topics in statistics traffic engineering computing Health and safety aspects basic architecture

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NEW AUTOMATIC VEHICLE IDENTIFICATION system FOR DETECTION OF traffic WITHOUT LANE DISCIPLINE

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ELECTRONICS & COMMUNICATION ENGINEERING JOURNAL 1991年第3期3卷 99-107页

作者： DO, MA ONG, JT School of Electrical & Electronic Engineering Nanyang Technological Institute Singapore Singapore

Electronic road toll collection has usually been carried out on the highway using an automatic vehicle identification (AVI) system. At the toll site, physical barriers are set up between lanes so that individual vehicles can be effectively detected. Problems arise, however, in the detection of vehicles on busy city roads where barriers between lanes are not desirable and the traffic does not always maintain lane discipline. A new AVI system using a randomised time division multiplexing (TDM) technique for detecting several vehicles simultaneously entering the detection zone is described in this paper. Laboratory experiments carried out for up to eight identification tags at the same time showed that all replies were detected for 900 interrogations. Collisions between replying signals did not occur. The high-speed transmission of serial data has, however, an error rate of 3-7%. Under laboratory conditions, the detection errors were eliminated simply by allowing the tags to reply twice.

关键词： serial data data communication systems laboratory experiments Computer networks and techniques error rate road vehicles road traffic detection road traffic highway Switching centres and equipment time division multiplexing Computer communications electronic road toll collection high-speed transmission TDM road-traffic system control automatic vehicle identification system randomised time division multiplexing detection errors

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