Pub Date : 2024-09-02DOI: 10.1109/TITS.2024.3445391
Srivalli Boddupalli;Chung-Wei Lin;Sandip Ray
Cooperative Adaptive Cruise Control (CACC) is a fundamental connected vehicle application. In CACC, a vehicle coordinates its longitudinal movements to safely and efficiently follow the vehicle in front. The follower vehicle relies on a combination of sensory and communication inputs to identify the position, velocity, and acceleration of the preceding vehicle. Malicious subversion of these inputs can cause catastrophic accidents, string instability, and disruption in the transportation infrastructure. In this paper, we develop a security system, ReCAP, to provide real-time resiliency in CACC against adversarial subversion of both sensory and communication inputs. ReCAP makes use of a combination of techniques based on kinematics and machine learning to detect anomalous inputs, narrow down the source of subversion, and perform mitigation. We provide extensive simulations to demonstrate the effectiveness of ReCAP against a diverse spectrum of attacks under complex, multi-channel adversaries.
{"title":"ReCAP: Protecting Cooperative Adaptive Cruise Control Against Multi-Channel Perception Adversary","authors":"Srivalli Boddupalli;Chung-Wei Lin;Sandip Ray","doi":"10.1109/TITS.2024.3445391","DOIUrl":"10.1109/TITS.2024.3445391","url":null,"abstract":"Cooperative Adaptive Cruise Control (CACC) is a fundamental connected vehicle application. In CACC, a vehicle coordinates its longitudinal movements to safely and efficiently follow the vehicle in front. The follower vehicle relies on a combination of sensory and communication inputs to identify the position, velocity, and acceleration of the preceding vehicle. Malicious subversion of these inputs can cause catastrophic accidents, string instability, and disruption in the transportation infrastructure. In this paper, we develop a security system, ReCAP, to provide real-time resiliency in CACC against adversarial subversion of both sensory and communication inputs. ReCAP makes use of a combination of techniques based on kinematics and machine learning to detect anomalous inputs, narrow down the source of subversion, and perform mitigation. We provide extensive simulations to demonstrate the effectiveness of ReCAP against a diverse spectrum of attacks under complex, multi-channel adversaries.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15702-15717"},"PeriodicalIF":7.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/TITS.2024.3415435
Ze Zhang;Yue Yao;Windo Hutabarat;Michael Farnsworth;Divya Tiwari;Ashutosh Tiwari
Connected autonomous vehicles (CAVs) offer significant enhancements in coordinated traffic and safety through real-time vehicle-to-vehicle or vehicle-to-infrastructure communications, establishing them as a potent tool for augmenting driving tasks. However, the extensive information-sharing framework inherent in CAVs amplifies the risk associated with sensor anomalies, posing challenges to the reliability and security of the system. Responding to this timely research challenge, this study proposes a novel anomaly detection method, namely Dual-channel Self-attention-based Convolutional Neural Network (DSA-CNN) for multivariate time series data. Through the introduction of the Dual-channel Self-attention Mechanism, DSA-CNN can progressively and autonomously extract spatiotemporal features from multivariate time series data. The proposed method was tested under a variety of common threatening sensor anomaly patterns of CAVs summarised in the literature, and evaluated under multiple different performance metrics. The results demonstrate its advantages in detecting minor anomalies and enhancing sensitivity, outperforming previously reported methods in the literature. Across all experimental scenarios, an average sensitivity improvement of 2.53% was observed, complemented by an average F1 score increase of 1.47%. In CAV settings, maintaining high sensitivity to ensure fewer undetected anomalies, alongside the ability to detect small anomalies, can be more important for the robustness and safety measures of CAV systems.
{"title":"Time Series Anomaly Detection in Vehicle Sensors Using Self-Attention Mechanisms","authors":"Ze Zhang;Yue Yao;Windo Hutabarat;Michael Farnsworth;Divya Tiwari;Ashutosh Tiwari","doi":"10.1109/TITS.2024.3415435","DOIUrl":"10.1109/TITS.2024.3415435","url":null,"abstract":"Connected autonomous vehicles (CAVs) offer significant enhancements in coordinated traffic and safety through real-time vehicle-to-vehicle or vehicle-to-infrastructure communications, establishing them as a potent tool for augmenting driving tasks. However, the extensive information-sharing framework inherent in CAVs amplifies the risk associated with sensor anomalies, posing challenges to the reliability and security of the system. Responding to this timely research challenge, this study proposes a novel anomaly detection method, namely Dual-channel Self-attention-based Convolutional Neural Network (DSA-CNN) for multivariate time series data. Through the introduction of the Dual-channel Self-attention Mechanism, DSA-CNN can progressively and autonomously extract spatiotemporal features from multivariate time series data. The proposed method was tested under a variety of common threatening sensor anomaly patterns of CAVs summarised in the literature, and evaluated under multiple different performance metrics. The results demonstrate its advantages in detecting minor anomalies and enhancing sensitivity, outperforming previously reported methods in the literature. Across all experimental scenarios, an average sensitivity improvement of 2.53% was observed, complemented by an average F1 score increase of 1.47%. In CAV settings, maintaining high sensitivity to ensure fewer undetected anomalies, alongside the ability to detect small anomalies, can be more important for the robustness and safety measures of CAV systems.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15964-15976"},"PeriodicalIF":7.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a joint trajectory optimization algorithm for a number of connected and automated vehicles in a lane-free traffic environment with vehicle nudging. A double double-integrator model is utilized for the longitudinal and lateral movements of each vehicle. The objective function consists of several sub-objectives that reflect corresponding, partially competing driving aspects and concerns, including passenger comfort, low fuel consumption, vehicle advancing at desired speed, collision avoidance, suppressing of infeasible maneuvers. Fixed and state-dependent control input bounds account for various technical limitations as well as for road boundary respect. The solution of the formulated joint Optimal Control Problem (OCP) is computed by use of a very efficient Feasible Direction Algorithm, which exploits the structure of the state equations to map the OCP into a reduced Nonlinear Programming Problem. To demonstrate the efficiency of the proposed approach, challenging scenarios are examined on a lane-free straight motorway stretch. The results of the centralized (joint) OCP are compared with a previously investigated decentralized approach where OCPs are employed separately for individual vehicles.
{"title":"Joint Path Planning for Multiple Automated Vehicles in Lane-Free Traffic With Vehicle Nudging","authors":"Niloufar Dabestani;Panagiotis Typaldos;Venkata Karteek Yanumula;Ioannis Papamichail;Markos Papageorgiou","doi":"10.1109/TITS.2024.3445501","DOIUrl":"10.1109/TITS.2024.3445501","url":null,"abstract":"This article presents a joint trajectory optimization algorithm for a number of connected and automated vehicles in a lane-free traffic environment with vehicle nudging. A double double-integrator model is utilized for the longitudinal and lateral movements of each vehicle. The objective function consists of several sub-objectives that reflect corresponding, partially competing driving aspects and concerns, including passenger comfort, low fuel consumption, vehicle advancing at desired speed, collision avoidance, suppressing of infeasible maneuvers. Fixed and state-dependent control input bounds account for various technical limitations as well as for road boundary respect. The solution of the formulated joint Optimal Control Problem (OCP) is computed by use of a very efficient Feasible Direction Algorithm, which exploits the structure of the state equations to map the OCP into a reduced Nonlinear Programming Problem. To demonstrate the efficiency of the proposed approach, challenging scenarios are examined on a lane-free straight motorway stretch. The results of the centralized (joint) OCP are compared with a previously investigated decentralized approach where OCPs are employed separately for individual vehicles.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"18525-18536"},"PeriodicalIF":7.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1109/tits.2024.3445156
Tomás de J. Mateo Sanguino, José Manuel Lozano Domínguez, Manuel Joaquín Redondo González, Jose Miguel Davila Martin
{"title":"New Approach to Intelligent Pedestrian Detection and Signaling on Crosswalks","authors":"Tomás de J. Mateo Sanguino, José Manuel Lozano Domínguez, Manuel Joaquín Redondo González, Jose Miguel Davila Martin","doi":"10.1109/tits.2024.3445156","DOIUrl":"https://doi.org/10.1109/tits.2024.3445156","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"4 1","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1109/TITS.2024.3444048
Piotr PiąTek;Piotr Mydłowski;Aleksander Buczacki;Szczepan Moskwa
The automotive industry is undergoing significant changes due to increased connectivity, data usage, and vehicle autonomy, which pose new challenges and increase the attack surface of vehicles. To effectively address these challenges, all design tasks in automotive projects need to be well-coordinated and prioritize vehicle security. Model-Based Systems Engineering (MBSE) provides a comprehensive approach that allows multiple engineering disciplines to work concurrently. In this study, we propose the integration of well-established security solutions, such as Security Patterns, into safety-critical automotive systems using the MBSE approach. Our work presents a procedural flow for incorporating Security Patterns into the system model, emphasizing the inclusion of cybersecurity (CySe) and functional safety (FS) actions. To meet the regulatory requirements, we selected the IDS (Intrusion Detection System) pattern as a key component of our proposed CyberSafety Design Framework. In a real-world case study of an Advanced Emergency Braking System (AEBS), we evaluated the effectiveness of our framework by integrating the IDS pattern with TARA and HARA assessments. Our results demonstrate the feasibility of merging design processes within an MBSE framework, reducing design effort and aligning with the security by design principle. Future research should explore the application of different Security Patterns in conjunction with SOTIF systems, and industry efforts should be directed towards standardizing the collaboration between cybersecurity and safety.
{"title":"Concept of Using the MBSE Approach to Integrate Security Patterns in Safety-Related Projects for the Automotive Industry","authors":"Piotr PiąTek;Piotr Mydłowski;Aleksander Buczacki;Szczepan Moskwa","doi":"10.1109/TITS.2024.3444048","DOIUrl":"10.1109/TITS.2024.3444048","url":null,"abstract":"The automotive industry is undergoing significant changes due to increased connectivity, data usage, and vehicle autonomy, which pose new challenges and increase the attack surface of vehicles. To effectively address these challenges, all design tasks in automotive projects need to be well-coordinated and prioritize vehicle security. Model-Based Systems Engineering (MBSE) provides a comprehensive approach that allows multiple engineering disciplines to work concurrently. In this study, we propose the integration of well-established security solutions, such as Security Patterns, into safety-critical automotive systems using the MBSE approach. Our work presents a procedural flow for incorporating Security Patterns into the system model, emphasizing the inclusion of cybersecurity (CySe) and functional safety (FS) actions. To meet the regulatory requirements, we selected the IDS (Intrusion Detection System) pattern as a key component of our proposed CyberSafety Design Framework. In a real-world case study of an Advanced Emergency Braking System (AEBS), we evaluated the effectiveness of our framework by integrating the IDS pattern with TARA and HARA assessments. Our results demonstrate the feasibility of merging design processes within an MBSE framework, reducing design effort and aligning with the security by design principle. Future research should explore the application of different Security Patterns in conjunction with SOTIF systems, and industry efforts should be directed towards standardizing the collaboration between cybersecurity and safety.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15477-15492"},"PeriodicalIF":7.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1109/TITS.2024.3447700
Shui Fu;Wentao Tang;Rui Wang;Si-Xin Wen;Xi-Ming Sun
Efficient fault-tolerant control (FTC) is necessary for the safe operation of aero-engine control system. In this paper, a high performance active FTC method based on zonotope for actuator fault in aero-engine control systems is proposed. Parameter uncertainties are considered to describe linearization error and identification error of system model for reducing the gap between theory and practice. Firstly, a zonotopic observer satisfying the peak-bounded index is proposed to reduce the influence of uncertainties and improve the accuracy of fault estimation. Moreover, with the aid of the zonotopic observer, the range of the sliding surface affected by the estimation errors and model uncertainties can be evaluated, and the dynamic quasi-sliding mode domain (QSMD) can be obtained. As a result, the dynamic QSMD can help design the parameters of the sliding mode fault-tolerant controller, ensure the stability and convergence of the entire closed-loop control system. Meanwhile, the conservative problem caused by manual parameters setting is avoided. Finally, the feasibility of the proposed method is verified by the aero-engine Hardware-in-the-loop (HIL) experiment platform.
{"title":"Actuator Fault-Tolerant Control for Aero-Engine Control System: A Zonotope-Based Approach","authors":"Shui Fu;Wentao Tang;Rui Wang;Si-Xin Wen;Xi-Ming Sun","doi":"10.1109/TITS.2024.3447700","DOIUrl":"10.1109/TITS.2024.3447700","url":null,"abstract":"Efficient fault-tolerant control (FTC) is necessary for the safe operation of aero-engine control system. In this paper, a high performance active FTC method based on zonotope for actuator fault in aero-engine control systems is proposed. Parameter uncertainties are considered to describe linearization error and identification error of system model for reducing the gap between theory and practice. Firstly, a zonotopic observer satisfying the peak-bounded index is proposed to reduce the influence of uncertainties and improve the accuracy of fault estimation. Moreover, with the aid of the zonotopic observer, the range of the sliding surface affected by the estimation errors and model uncertainties can be evaluated, and the dynamic quasi-sliding mode domain (QSMD) can be obtained. As a result, the dynamic QSMD can help design the parameters of the sliding mode fault-tolerant controller, ensure the stability and convergence of the entire closed-loop control system. Meanwhile, the conservative problem caused by manual parameters setting is avoided. Finally, the feasibility of the proposed method is verified by the aero-engine Hardware-in-the-loop (HIL) experiment platform.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"18861-18871"},"PeriodicalIF":7.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of drones into shared airspace for beyond visual line of sight (BVLOS) operations presents significant challenges but holds transformative potential for sectors like transportation, construction, energy and defence. A prerequisite for this integration is equipping drones with enhanced situational awareness to ensure collision avoidance and safe operations. Current approaches mainly target single object detection or classification, or simpler sensing outputs that offer limited perceptual understanding and lack the rapid end-to-end processing needed to convert sensor data into safety-critical insights. In contrast, our study leverages radar technology for novel end-to-end semantic segmentation of aerial point clouds to simultaneously identify multiple collision hazards. By adapting and optimizing the PointNet architecture and integrating aerial domain insights, our framework distinguishes five distinct classes: mobile targets like drones (DJI M300 and DJI Mini) and airplanes (Ikarus C42), and static returns (ground and infrastructure) which results in enhanced situational awareness for drones. To our knowledge, this is the first approach addressing simultaneous identification of multiple collision threats in an aerial setting, achieving a robust 94% accuracy. This work highlights the potential of radar technology to advance situational awareness in drones, facilitating safe and efficient BVLOS operations.
{"title":"Segmentation of Drone Collision Hazards in Airborne RADAR Point Clouds Using PointNet","authors":"Hector Arroyo;Paul Keir;Dylan Angus;Santiago Matalonga;Svetlozar Georgiev;Mehdi Goli;Gerard Dooly;James Riordan","doi":"10.1109/TITS.2024.3442668","DOIUrl":"10.1109/TITS.2024.3442668","url":null,"abstract":"The integration of drones into shared airspace for beyond visual line of sight (BVLOS) operations presents significant challenges but holds transformative potential for sectors like transportation, construction, energy and defence. A prerequisite for this integration is equipping drones with enhanced situational awareness to ensure collision avoidance and safe operations. Current approaches mainly target single object detection or classification, or simpler sensing outputs that offer limited perceptual understanding and lack the rapid end-to-end processing needed to convert sensor data into safety-critical insights. In contrast, our study leverages radar technology for novel end-to-end semantic segmentation of aerial point clouds to simultaneously identify multiple collision hazards. By adapting and optimizing the PointNet architecture and integrating aerial domain insights, our framework distinguishes five distinct classes: mobile targets like drones (DJI M300 and DJI Mini) and airplanes (Ikarus C42), and static returns (ground and infrastructure) which results in enhanced situational awareness for drones. To our knowledge, this is the first approach addressing simultaneous identification of multiple collision threats in an aerial setting, achieving a robust 94% accuracy. This work highlights the potential of radar technology to advance situational awareness in drones, facilitating safe and efficient BVLOS operations.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"17762-17777"},"PeriodicalIF":7.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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