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Anomaly recognition method of perception system for autonomous vehicles based on distance metric

ELECTRONICS LETTERS 2022年第20期58卷 774-776页

作者： Shao, Cuiping Chen, Beizhang Miao, Zujia Cui, Yunduan Li, Huiyun Chinese Acad Sci Shenzhen Inst Adv Technol 1068 Xueyuan Ave Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Environmental perception system is the premise of the safety and stability of the autonomous vehicle system. However, studies have shown that the on-board sensors included in the perception system are extremely vulnerable to external attacks and interference, leading to incorrect driving strategies and bringing great security threats. Aiming at the problem, this paper divides the vehicle-mounted sensors into a positioning group and an identification group according to their role in the perception system. Then, based on the information correlation between sensors in the same group and the information correlation of a single sensor on adjacent time series, the distance metric model between sensors in a group and the distance metric model for each sensor of this group on time series is established. And the normal distance intervals corresponding to the confidence interval are calculated respectively. According to the distance metric model between sensors, the authors can detect anomalies in the perception system in real time. Further, according to the distance metric model for each sensor on adjacent time series, the authors can identify anomaly sensors. The authors' experimental results quantitatively show that the method achieves real-time anomaly recognition, and demonstrate the effectiveness and robustness of the method on the open-source Karlsruhe institute of technology and toyota technological institute (KITTI) dataset.

关键词： incorrect driving strategies mobile robots intelligent transportation systems autonomous vehicles anomaly sensors positioning group feature extraction object detection time series distance metric model adjacent time series computer network security anomaly recognition method single sensor on-board sensors autonomous vehicle system vehicle-mounted sensors traffic engineering computing identification group environmental perception system real-time anomaly recognition information correlation bringing great security threats normal distance intervals

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CT2-MDS: Cooperative trust-aware tolerant misbehaviour detection system for connected and automated vehicles

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第2期16卷 218-231页

作者： Liu, Ying Xue, Hongwei Zhuang, Weichao Wang, Fa'an Xu, Liwei Yin, Guodong Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China

Connected and automated vehicles (CAVs) are recognized as a promising solution to improve road safety. Before deploying CAVs, securing the communication environment is a critical challenge. To secure the exchanged sensitive messages between CAVs, a novel cooperative trust-aware tolerant misbehaviour detection system (CT2-MDS) is proposed in this paper. The vehicle-to-everything (V2X) network model in real complex scenarios is built first, and several typical types of attacks are simulated. The cooperative trust factor is then defined to evaluate the received message packet's plausibility instead of the node. And the measuring uncertainty of sensors is considered to improve the overall detection precision. At last, a multi-model fusion misbehaviour detection method is presented based on the idea of cross-validation. In this mechanism, different from the binary classification, the misbehaviour messages are categorized into particular misbehaving classes. To verify the proposed system's performance, the simulation is conducted, and the results showed that the proposed method outperformed existing single classifiers and provided good prediction accuracy in different attack density.

关键词： multimodel fusion misbehaviour detection method exchanged sensitive messages road vehicles vehicle-to-everything network model computer network security intelligent transportation systems received message packet trust-aware tolerant misbehaviour detection system communication environment V2X road safety trusted computing vehicular ad hoc networks novel cooperative trust-aware CT2-MDS detection precision trust factor pattern classification misbehaviour messages traffic engineering computing connected automated vehicles

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Handling stability control strategy for four-wheel hub motor-driven vehicle based on adaptive control for road adhesion

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第5期16卷 586-601页

作者： Zhai, Li Wang, Chengping Zhang, Xueying Hou, Rufei Mok, Yuh Ming Hou, Yuhan Beijing Inst Technol Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China Shanghai Electromech Engn Inst Shanghai Peoples R China

Handling stability control can improve the safety of four in-wheel motor independent-drive electric vehicle (4MIDEV). An adaptive motion state control strategy based on improved sliding mode control for 4MIDEV is proposed to improve the steering stability control of the vehicle under different adhesion conditions. This adaptive control strategy uses a multi-layer structure, which mainly includes a motion reference layer, an upper-layer motion controller, and a lower-layer torque distribution controller. Aiming at the non-linear dynamic characteristics of 4MIDEV under different adhesion conditions, a dynamically parameter-varying motion reference layer is constructed to improve the accuracy of the reference values of sideslip angle and yaw rate, and realize the joint control of them. Considering the adhesion coefficient, construct the comprehensive yaw rate state variable, and use the improved sliding mode control algorithm to calculate the expected yaw moment. The lower-level controller uses the optimal distribution method to distribute the torque of each drive wheel in combination with the adhesion coefficient. By constructing a joint simulation platform of Carsim and Simulink, simulations under different road adhesion conditions are realized, verifying that the proposed improved strategy can effectively adapt to different adhesion conditions, while improve the handling stability of 4MIDEV.

关键词： 4MIDEV steering systems road adhesion conditions steering stability control multilayer structure nonlinear dynamic characteristics optimal distribution method electric vehicles road vehicles lower-layer torque distribution controller control engineering computing nonlinear dynamical systems adaptive control optimal control dynamically parameter-varying motion reference layer adaptive motion state control Carsim distributed control vehicle dynamics Simulink wheels stability motion control handling stability control variable structure systems sliding mode control upper-layer motion controller four-wheel hub motor-driven vehicle sideslip angle adhesion four in-wheel motor independent-drive electric vehicle yaw rate state variable torque control traffic engineering computing adhesion coefficient

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QPNet: Lane-changing trajectory planning combining quadratic programming and neural network under the convex optimization framework

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第11期16卷 1578-1599页

作者： Wang, Ying Wei, Chong Li, Shurong Beijing Jiaotong Univ Sch Traff & Transportat Key Lab Transport Ind Big Data Applicat Technol C Minist Transport Beijing 100044 Peoples R China

Lane-changing is a basic driving behaviour, which largely impacts on traffic safety and efficiency. With the development of technology, the automated lane-changing system has attracted extensive attention. Among it, the trajectory planning part is a challenging problem owing to the complexity and diversity of the driving situations. The planner requires the real-time capability to produce safe and comfortable trajectories for coping with the dynamically changing environment. Based on this, the paper proposes a lane-changing trajectory planning model in dynamic driving environments. The model constructs a neural network to predict the end position of the ego vehicle, and then adopts the mathematical programming method to solve the optimal lane-changing trajectory that guarantees the ride safety and comfort. With the assistance of the neural network, the lane-changing trajectory planning problem is converted into a quadratic programming (QP) model, thereby achieving rapid solution of the model. Moreover, to train the proposed algorithm, a novel approach for generating the scenario data is designed, which can generate rich and diverse traffic scenarios at a low cost. The simulation results show that the proposed model can plan a lane-changing trajectory quickly and effectively, and the ego vehicle can follow the planned trajectory safely and comfortably.

关键词： rich traffic scenarios traffic safety ride safety optimal lane-changing trajectory trajectory planning part road vehicles intelligent transportation systems driving situations trajectory control convex programming optimisation planned trajectory path planning convex optimization framework basic driving behaviour safe trajectories road traffic comfortable trajectories neural network quadratic programming model road safety road traffic control dynamic driving environments lane-changing trajectory planning problem automated lane-changing system quadratic programming dynamically changing environment diverse traffic scenarios lane-changing trajectory planning model ego vehicle mathematical programming method traffic engineering computing

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Directed hypergraph attention network for traffic forecasting

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第1期16卷 85-98页

作者： Luo, Xiaoyi Peng, Jiaheng Liang, Jun Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China

In traffic systems, traffic forecasting is a critical issue, which has attracted much interest from researchers. It is a challenging task due to the complex spatial-temporal patterns of traffic data. Previous works focus on designing complex graph-based neural networks to model spatial-temporal dependencies from data. By using graphs to represent road networks, these works capture spatial patterns with graph convolutions. However, graphs cannot fully represent spatial relations from road networks. It limits the performance of graph-based methods. In this paper, we propose a directed hypergraph neural network architecture, Directed Hypergraph Attention Network(DHAT), for traffic forecasting. Unlike previous works, DHAT introduces a directed hypergraph to represent road networks. Compared with graphs, directed hypergraphs could represent spatial information from graphs and outperform them in modeling complex directed relations among multiple nodes. Based on the directed hypergraph, a directed hypergraph convolution is proposed to exploit spatial relations among traffic series. By combining the proposed convolution and attention mechanisms, DHAT can effectively achieve promising predictions for traffic forecasting. To evaluate the performance of DHAT, we have conducted extensive experiments on four real-world traffic datasets. Compared with other baselines, experimental results show that DHAT reduces Mean Absolute Error by 0.03-0.64 on these datasets.

关键词： spatial information pattern clustering traffic data directed hypergraph neural network architecture attention mechanisms previous works focus graph theory learning (artificial intelligence) real-world traffic datasets spatial-temporal patterns neural nets graph-based methods road traffic graph convolutions traffic forecasting complex graph-based neural networks traffic series spatial relations complex directed relations directed hypergraph convolution traffic systems road networks works capture spatial patterns spatial-temporal dependencies traffic engineering computing telecommunication traffic

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A multi-channel geometric algebra residual network for traffic data prediction

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第11期16卷 1549-1560页

作者： Zang, Di Chen, Xihao Lei, Juntao Wang, Zengqiang Zhang, Junqi Cheng, Jiujun Tang, Keshuang Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China Tongji Univ Dept Traff Informat Engn & Control Shanghai Peoples R China

traffic data prediction offers a significant way to evaluate the future traffic congestion status;many deep learning based approaches have been widely applied in this field. Most current methods only consider short-term traffic data forecasting;however, long-term prediction, which supports the optimized distribution of traffic resources, is not well studied. Besides, multiple traffic parameters enable stronger constraints for the data estimation, but the correlation between them in both spatial and temporal domains has not been efficiently learned. Geometric algebra, as a generalization of linear algebra, provides a framework to encode multidimensional data and analyze the correlation. By combining the advantages of the deep neural network and geometric algebra, a multi-channel geometric algebra residual network (MGAResNet) is proposed to address the problem of long-term traffic data prediction. traffic data obtained from two urban expressways are employed and experimental results demonstrate that the approach outperforms the state-of-the-art work.

关键词： traffic resources long-term traffic data prediction multichannel geometric algebra residual network long-term prediction deep learning based approaches future traffic congestion status multidimensional data learning (artificial intelligence) data estimation deep neural network short-term traffic data forecasting multiple traffic parameters deep learning (artificial intelligence) linear algebra road traffic traffic engineering computing telecommunication traffic

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Identification and evaluation of the effective criteria for detection of congestion in a smart city

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IET COMMUNICATIONS 2022年第5期16卷 560-570页

作者： Mohanty, Anita Mohanty, Subrat Kumar Jena, Bhagyalaxmi Mohapatra, Ambarish G. Rashid, Ahmed N. Khanna, Ashish Gupta, Deepak Silicon Inst Technol Elect & Instrumentat Engn Bhubaneswar Odisha India Coll Engn Bhubaneswar Elect & Telecommun Engn Bhubaneswar Odisha India Silicon Inst Technol Elect & Commun Engn Bhubaneswar Odisha India Silicon Inst Technol Elect & Instrumentat Engn Bhubaneswar Odisha India Univ Anbar Coll Comp Sci & Informat Technol Ramadi Iraq Maharaja Agrasen Inst Technol Comp Sci Engn Delhi India Maharaja Agrasen Inst Technol Comp Sci Engn Delhi India

The delay in transportation of necessary items is due to traffic congestion throughout the world. This is a serious phenomenon which results in waste of time and fuel. The detection of road conditions and dissemination of traffic information efficiently and effectively is a big challenge to authorities. Recently, the technologies of vehicular ad hoc networks (VANETs) have been utilized and become an important part of the intelligent transportation system (ITS). For this existing problem, vehicle-to-vehicle (V2V) communication provides a means for cooperation and route management in transport networks. This paper proposed a novel congestion detection system based on the combination of k-means clustering and analytical hierarchy process. In the simulation of urban mobility (SUMO) simulator, a transport network is created and parameters of vehicles facing congestion are taken to extract the key parameter by using the k-means clustering technique and mathematical mean algorithm. This parameter is utilized in analytical hierarchy process to detect the highest priorities parameter and based on that the congestion is detected in particular lane. The result can be a better technique for congestion detection as it requires low installation cost and can be incorporate in vehicles for congestion avoidance which will alternatively improve the traffic flow.

关键词： analytic hierarchy process smart cities vehicle-to-vehicle communication intelligent transportation systems road traffic road conditions traffic flow urban mobility simulator congestion avoidance intelligent transportation system traffic information transport network mathematical mean algorithm traffic congestion analytical hierarchy process traffic engineering computing vehicular ad hoc networks route management congestion detection system smart city

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traffic forecasting with missing data via low rank dynamic mode decomposition of tensor

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第9期16卷 1164-1176页

作者： Zhang, Xinyu Zhang, Yong Wei, Xiulan Hu, Yongli Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Key Lab Multimedia & Intelligent Software Beijing Inst Artificial Intelligence 100 Pingleyuan St Beijing Peoples R China Beijing Univ Technol Coll Metropolitan Transportat Beijing Inst Artificial Intelligence Beijing Key Multimedia & Intelligent Software Tec Beijing Peoples R China

traffic forecasting is an important part in realising intelligent traffic management, which helps traffic controllers and travellers make effective decisions. However, traffic forecasting accuracy is often affected by missing traffic data due to hardware and software failure. Therefore, accurate prediction based on incomplete traffic data is an important problem as well as a challenge. Though many approaches recover the missing values before prediction, the errors from the data-filling step are likely to cause additional bias to the prediction result. Besides, this tactic is difficult to guarantee the timeliness and may impede real-time prediction. In this case, a traffic forecasting model is proposed to directly predict traffic data with missing values. This model develops tensor formed dynamic mode decomposition, recording the dynamic information of traffic data into a state transition tensor. In addition, the model takes low rank property of the dynamic tensor and the similarity of temporal variation trend into consideration. In order to verify the effectiveness and the robustness of the proposed model, experiments were performed on two real-world time series datasets. The results demonstrate that the model achieves better performance on forecasting than other baseline approaches under the impact of missing data.

关键词： data-filling step low rank dynamic mode decomposition tensor formed dynamic mode decomposition dynamic tensor real-time prediction intelligent traffic management traffic forecasting accuracy time series traffic forecasting model tensors road traffic traffic engineering computing incomplete traffic data traffic controllers

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A classification method for urban functional regions based on the transfer rate of empty cars

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第2期16卷 133-147页

作者： Xu, Zhihao Li, Jianbo Lv, Zhiqiang Dong, Chuanhao Fu, Liping Qingdao Univ Coll Comp Sci & Technol 308 Ningxia Rd Qingdao Shandong Peoples R China Qingdao Univ Inst Ubiquitous Networks & Urban Computing 308 Ningxia Rd Qingdao Shandong Peoples R China

Predicting the nature of each urban functional region based on the transfer rate of empty cars plays a crucial role in constructing smart cities and urban planning. The transfer rate of empty cars describes the probability of a taxi driving from one region to another without any passengers. It can reflect the main driving directions of taxies and the main flow directions of people among different urban functional regions. Although current researches have focused on the functional regions divided by remote sensing satellite images, there is almost no discussion on determining the nature of the region through taxi behaviour. The authors consider using taxi behaviour to classify urban functional regions. Besides, the attentional spatio-temporal model (Attentional Gated Recurrent Unit, AGRU) is introduced in the work. The AGRU consists of three modules, which are the spatial feature extraction module, the temporal feature extraction module, and the attentional pooling mechanism. The model has been evaluated on the data set provided by Didi Chuxing and it has been compared with some typical models. The experimental results show that the AGRU can reflect spatio-temporal information, and its attentional pooling mechanism can distinguish whether a region is the place of departure or the destination.

关键词： image classification recurrent neural nets remote sensing different urban functional regions smart cities geophysical image processing transfer rate urban functional region urban planning spatiotemporal phenomena cars feature extraction taxi behaviour traffic engineering computing town and country planning

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Cyber-physical description and CPS-based pinning approach of mixed traffic

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第3期16卷 344-362页

作者： Liu, Zhongcheng Sun, Dihua Zhao, Min Zhao, Hang Wang, Shihui Liao, Xiaoyong Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China

In this paper, a cyber-physical description of mixed traffic that can reflect the interaction between vehicles is first proposed. By treating vehicles as nodes and the cyber-physical interaction between vehicles as directed edges between nodes, the network of vehicles in traffic is obtained. The small-world and scale-free characteristics of mixed traffic are found under this description. Then the CPS-based pinning approach for achieving indirect control of HVs is proposed. Besides the control algorithm of pinning nodes, the impact of the spatial distribution of automated vehicles on traffic is studied. The approach is compatible with the existing control of automated vehicle in mixed vehicle platoon. Numerical simulation results under three-lane scenario illustrated the effectiveness of the proposed pinning approach.

关键词： scale-free characteristics road vehicles numerical simulation numerical analysis CPS-based pinning mixed vehicle platoon small-world networks small-world characteristics road traffic control cyber-physical interaction mixed traffic automated vehicle control cyber-physical description human-driven vehicle control spatial distribution three-lane scenario pinning nodes cyber-physical systems indirect HV control traffic engineering computing

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