Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Wang, Boyu; Li, Wan; Khattak, Zulqarnain H.

doi:10.3390/electronics13071251

Title: Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Journal Article · 28 March 2024 · Electronics

DOI: https://doi.org/10.3390/electronics13071251 · OSTI ID:2333749

Wang, Boyu ^[1];

^[2];

^[3]

Tacoma Public Utilities, Tacoma, WA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Carnegie Mellon Univ., Pittsburgh, PA (United States)

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2333749

Journal Information:: Electronics, Vol. 13, Issue 7; ISSN 2079-9292

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

References (35)

A Secure Sensor Fusion Framework for Connected and Automated Vehicles Under Sensor Attacks Yang, Tianci; Lv, Chen IEEE Internet of Things Journal, Vol. 9, Issue 22 https://doi.org/10.1109/JIOT.2021.3101502	journal	November 2022
Security vulnerabilities of connected vehicle streams and their impact on cooperative driving Amoozadeh, Mani; Raghuramu, Arun; Chuah, Chen-nee IEEE Communications Magazine, Vol. 53, Issue 6 https://doi.org/10.1109/MCOM.2015.7120028	journal	June 2015
Impact of cyberattacks on safety and stability of connected and automated vehicle platoons under lane changes Khattak, Zulqarnain H.; Smith, Brian L.; Fontaine, Michael D. Accident Analysis & Prevention, Vol. 150 https://doi.org/10.1016/j.aap.2020.105861	journal	February 2021
Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network Javed, Abdul Rehman; Usman, Muhammad; Rehman, Saif Ur IEEE Transactions on Intelligent Transportation Systems, Vol. 22, Issue 7 https://doi.org/10.1109/TITS.2020.3025875	journal	July 2021
A Distributed Adaptive Triple-Step Nonlinear Control for a Connected Automated Vehicle Platoon With Dynamic Uncertainty Guo, Hongyan; Liu, Jun; Dai, Qikun IEEE Internet of Things Journal, Vol. 7, Issue 5 https://doi.org/10.1109/JIOT.2020.2973977	journal	May 2020
Falsified Data Attack on Backpressure-based Traffic Signal Control Algorithms Yen, Chia-Cheng; Ghosal, Dipak; Zhang, Michael 2018 IEEE Vehicular Networking Conference (VNC) https://doi.org/10.1109/VNC.2018.8628334	conference	December 2018
Time series anomaly detection for gravitational-wave detectors based on the Hilbert–Huang transform Son, Edwin J.; Kim, Whansun; Kim, Young-Min Journal of the Korean Physical Society, Vol. 78, Issue 10 https://doi.org/10.1007/s40042-021-00094-2	journal	March 2021
Assessing State Department of Transportation Readiness for Connected Vehicle–Cooperative Systems Deployment: Oregon Case Study Bertini, Robert L.; Wang, Haizhong; Knudson, Tony Transportation Research Record: Journal of the Transportation Research Board, Vol. 2559, Issue 1 https://doi.org/10.3141/2559-04	journal	January 2016
Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors Wang, Yiyang; Masoud, Neda; Khojandi, Anahita IEEE Transactions on Intelligent Transportation Systems, Vol. 22, Issue 3 https://doi.org/10.1109/TITS.2020.2970295	journal	March 2021
Cybersecurity Risk Analysis in the IoT: A Systematic Review AlSalem, Thanaa; Almaiah, Mohammed; Lutfi, Abdalwali Electronics, Vol. 12, Issue 18 https://doi.org/10.3390/electronics12183958	journal	September 2023
WALNUT: Waging Doubt on the Integrity of MEMS Accelerometers with Acoustic Injection Attacks Trippel, Timothy; Weisse, Ofir; Xu, Wenyuan 2017 IEEE European Symposium on Security and Privacy (EuroS&P) https://doi.org/10.1109/EuroSP.2017.42	conference	April 2017
Toward Trusted and Secure Communication Among Multiple Internal Modules in CAV Zhong, Hong; Cao, Wenwen; Zhang, Qingyang IEEE Internet of Things Journal, Vol. 8, Issue 24 https://doi.org/10.1109/JIOT.2021.3082542	journal	December 2021
A p-Opportunistic Channel Access Scheme for Interference Mitigation Between V2V and V2I Communications Wen, Xiangming; Chen, Jenhui; Hu, Zhiqun IEEE Internet of Things Journal, Vol. 7, Issue 5 https://doi.org/10.1109/JIOT.2020.2967647	journal	May 2020
A new approach of moving average method in time series analysis Hansun, Seng 2013 Conference on New Media Studies (CoNMedia) https://doi.org/10.1109/CoNMedia.2013.6708545	conference	November 2013
A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles Yun, Keon; Yun, Heesun; Lee, Sangmin Electronics, Vol. 13, Issue 2 https://doi.org/10.3390/electronics13020288	journal	January 2024
Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams Bhavsar, Parth; Das, Plaban; Paugh, Matthew Transportation Research Record: Journal of the Transportation Research Board, Vol. 2625, Issue 1 https://doi.org/10.3141/2625-06	journal	January 2017
Cybersecurity Attacks in Vehicle-to-Infrastructure Applications and Their Prevention Islam, Mhafuzul; Chowdhury, Mashrur; Li, Hongda Transportation Research Record: Journal of the Transportation Research Board, Vol. 2672, Issue 19 https://doi.org/10.1177/0361198118799012	journal	October 2018
BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data Ma, Mingrui; Han, Lansheng; Zhou, Chunjie Advanced Engineering Informatics, Vol. 56 https://doi.org/10.1016/j.aei.2023.101949	journal	April 2023
Integrating Plausibility Checks and Machine Learning for Misbehavior Detection in VANET So, Steven; Sharma, Prinkle; Petit, Jonathan 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) https://doi.org/10.1109/ICMLA.2018.00091	conference	December 2018
Anomaly detection in time series via robust PCA Jin, Yongjun; Qiu, Chenlu; Sun, Lei 2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE) https://doi.org/10.1109/ICITE.2017.8056937	conference	September 2017
Influence of cyber-attacks on longitudinal safety of connected and automated vehicles Li, Ye; Tu, Yu; Fan, Qi Accident Analysis & Prevention, Vol. 121 https://doi.org/10.1016/j.aap.2018.09.016	journal	December 2018
Dynamic risk assessment in autonomous vehicles motion planning Wardzinski, Andrzej 2008 1st International Conference on Information Technology https://doi.org/10.1109/INFTECH.2008.4621607	conference	May 2008
Development of a simulation platform for safety impact analysis considering vehicle dynamics, sensor errors, and communication latencies: Assessing cooperative adaptive cruise control under cyber attack Cui, Lian; Hu, Jia; Park, B. Brian Transportation Research Part C: Emerging Technologies, Vol. 97 https://doi.org/10.1016/j.trc.2018.10.005	journal	December 2018
A data-driven method for falsified vehicle trajectory identification by anomaly detection Huang, Shihong Ed; Feng, Yiheng; Liu, Henry X. Transportation Research Part C: Emerging Technologies, Vol. 128 https://doi.org/10.1016/j.trc.2021.103196	journal	July 2021
Impact Evaluation of Cyber-Attacks on Traffic Flow of Connected and Automated Vehicles Dong, Changyin; Wang, Hao; Ni, Daiheng IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.2993254	journal	January 2020
Securing Vehicle-to-Everything (V2X) Communication Platforms Hasan, Monowar; Mohan, Sibin; Shimizu, Takayuki IEEE Transactions on Intelligent Vehicles, Vol. 5, Issue 4 https://doi.org/10.1109/TIV.2020.2987430	journal	December 2020
Simulation Framework for Misbehavior Detection in Vehicular Networks Kamel, Joseph; Ansari, Mohammad Raashid; Petit, Jonathan IEEE Transactions on Vehicular Technology, Vol. 69, Issue 6 https://doi.org/10.1109/TVT.2020.2984878	journal	June 2020
Anomaly Detection Against GPS Spoofing Attacks on Connected and Autonomous Vehicles Using Learning From Demonstration Yang, Zhen; Ying, Jun; Shen, Junjie IEEE Transactions on Intelligent Transportation Systems, Vol. 24, Issue 9 https://doi.org/10.1109/TITS.2023.3269029	journal	September 2023
Development of a Robust Cooperative Adaptive Cruise Control With Dynamic Topology Cui, Lian; Chen, Zheng; Wang, Aobo IEEE Transactions on Intelligent Transportation Systems, Vol. 23, Issue 5 https://doi.org/10.1109/TITS.2020.3043194	journal	May 2022
Physical signal-driven fusion for V2X misbehavior detection Nguyen, Van-Linh; Lin, Po-Ching; Hwang, Ren-Hung 2019 IEEE Vehicular Networking Conference (VNC) https://doi.org/10.1109/VNC48660.2019.9062815	conference	December 2019
Impact of Security Attacks on Cooperative Driving Use Case: CACC Platooning Singh, Pranav Kumar; Saikamal Tabjul, Geetesh; Imran, Mohd TENCON 2018 - 2018 IEEE Region 10 Conference https://doi.org/10.1109/TENCON.2018.8650174	conference	October 2018
Investigating Cybersecurity Issues in Active Traffic Management Systems Khattak, Zulqarnain H.; Park, Hyungjun; Hong, Seongah Transportation Research Record: Journal of the Transportation Research Board, Vol. 2672, Issue 19 https://doi.org/10.1177/0361198118787636	journal	July 2018
Anomaly detection and string stability analysis in connected automated vehicular platoons Wang, Yiyang; Zhang, Ruixuan; Masoud, Neda Transportation Research Part C: Emerging Technologies, Vol. 151 https://doi.org/10.1016/j.trc.2023.104114	journal	June 2023
Aeromagnetic Anomaly Detection under Low SNR Conditions Using Multiscale Wavelet Energy Accumulation Zhou, Jiaxin; Zhang, Bo; Fan, Long 2020 IEEE 20th International Conference on Communication Technology (ICCT) https://doi.org/10.1109/ICCT50939.2020.9295710	conference	October 2020
Spreading Patterns of Malicious Information on Single‐Lane Platooned Traffic in a Connected Environment Wang, Pengcheng; Yu, Guizhen; Wu, Xinkai Computer-Aided Civil and Infrastructure Engineering, Vol. 34, Issue 3 https://doi.org/10.1111/mice.12416	journal	September 2018

Similar Records

Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using Long Short-Term Memory Autoencoder

Journal Article · 2023 · Energies · OSTI ID:1986565

Akins, Alexandra; Kultgen, Derek; Heifetz, Alexander

Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment

Journal Article · 2024 · Transportation Research, Part B: Methodological · OSTI ID:2376305

Wang, Jian; Zhou, Anye; Liu, Zhiyuan; +1 more

Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Journal Article · 2020 · IEEE Transactions on Industry Applications · OSTI ID:1810249

Zhang, Yue; Qin, Chuan; Srivastava, Anurag K.; +2 more

Related Subjects

33 ADVANCED PROPULSION SYSTEMS
cybersecurity
anomaly detection
falsified trajectories
CAVs
LSTM
Gaussian Mixture Model

Title: Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Citation Formats

References (35)

Similar Records

Related Subjects