Traffic flow digital twin generation for highway scenario based on radar-camera paired fusion

[1]  Siyang Cao,et al.  Robust Multiobject Tracking Using Mmwave Radar-Camera Sensor Fusion , 2022, IEEE Sensors Letters.

[2]  A. Eskandarian,et al.  State Estimation and Motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving: A Survey , 2022, IEEE Transactions on Intelligent Transportation Systems.

[3]  Sanghoon Lee,et al.  Design of V2X-Based Vehicular Contents Centric Networks for Autonomous Driving , 2022, IEEE Transactions on Intelligent Transportation Systems.

[4]  Md Zakirul Alam Bhuiyan,et al.  Digital Twin-Assisted Real-Time Traffic Data Prediction Method for 5G-Enabled Internet of Vehicles , 2022, IEEE Transactions on Industrial Informatics.

[5]  Simo Särkkä,et al.  Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review , 2020, IEEE Transactions on Intelligent Transportation Systems.

[6]  Peng Liu,et al.  Object Classification Based on Enhanced Evidence Theory: Radar–Vision Fusion Approach for Roadside Application , 2022, IEEE Transactions on Instrumentation and Measurement.

[7]  Supeng Leng,et al.  Digital Twin Based Trajectory Prediction for Platoons of Connected Intelligent Vehicles , 2021, 2021 IEEE 29th International Conference on Network Protocols (ICNP).

[8]  Muhammad Usman Shoukat,et al.  Autonomous Driving Test Method Based on Digital Twin: A Survey , 2021, 2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube).

[9]  Abdelhak M. Zoubir,et al.  Automotive Radar Signal Processing: Research Directions and Practical Challenges , 2021, IEEE Journal of Selected Topics in Signal Processing.

[10]  Antonios Tsourdos,et al.  Digital Twin Analysis to Promote Safety and Security in Autonomous Vehicles , 2021, IEEE Communications Standards Magazine.

[11]  Dirk Pesch,et al.  5G NR-V2X: Toward Connected and Cooperative Autonomous Driving , 2020, IEEE Communications Standards Magazine.

[12]  Xinggang Wang,et al.  FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking , 2020, International Journal of Computer Vision.

[13]  Bo Zhang,et al.  A Novel Multi-Sensor Fusion Based Object Detection and Recognition Algorithm for Intelligent Assisted Driving , 2021, IEEE Access.

[14]  Shunqiao Sun,et al.  MIMO Radar for Advanced Driver-Assistance Systems and Autonomous Driving: Advantages and Challenges , 2020, IEEE Signal Processing Magazine.

[15]  Zhangjing Wang,et al.  Multi-Sensor Fusion in Automated Driving: A Survey , 2020, IEEE Access.

[16]  Bin Yang,et al.  High-Performance Automotive Radar: A review of signal processing algorithms and modulation schemes , 2019, IEEE Signal Processing Magazine.

[17]  Lingyang Song,et al.  Cooperative Collision Avoidance for Overtaking Maneuvers in Cellular V2X-Based Autonomous Driving , 2019, IEEE Transactions on Vehicular Technology.

[18]  Lien-Wu Chen,et al.  Centimeter-Grade Metropolitan Positioning for Lane-Level Intelligent Transportation Systems Based on the Internet of Vehicles , 2019, IEEE Transactions on Industrial Informatics.

[19]  Rabie Ben Atitallah,et al.  Multi-Sensor Fusion for Obstacle Detection and Recognition: A Belief-Based Approach , 2018, 2018 21st International Conference on Information Fusion (FUSION).

[20]  Myo-Taeg Lim,et al.  Sensor fusion for vehicle tracking with camera and radar sensor , 2017, 2017 17th International Conference on Control, Automation and Systems (ICCAS).

[21]  Nanning Zheng,et al.  On-Road Vehicle Detection and Tracking Using MMW Radar and Monovision Fusion , 2016, IEEE Transactions on Intelligent Transportation Systems.

[22]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Marina Gashinova,et al.  Road Edge Recognition Using the Stripe Hough Transform From Millimeter-Wave Radar Images , 2015, IEEE Transactions on Intelligent Transportation Systems.

[24]  Shangguang Wang,et al.  An overview of Internet of Vehicles , 2014, China Communications.

[25]  Lena Maier-Hein,et al.  Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Simon Winberg,et al.  Multisensor data fusion: Target tracking with a doppler radar and an Electro-Optic camera , 2011, 2011 IEEE International Conference on Control System, Computing and Engineering.

[27]  Andriy Myronenko,et al.  Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  R. Altendorfer,et al.  A comparison of track-to-track fusion algorithms for automotive sensor fusion , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[29]  Rainer Stiefelhagen,et al.  Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics , 2008, EURASIP J. Image Video Process..

[30]  Juho Kannala,et al.  A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  W. Clem Karl,et al.  Line detection in images through regularized hough transform , 2006, IEEE Transactions on Image Processing.

[32]  Shu-Li Sun,et al.  Multi-sensor optimal information fusion Kalman filter , 2004, Autom..

[33]  I. T. Li,et al.  Multi-target multi-platform sensor registration in geodetic coordinates , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[34]  Yifeng Zhou,et al.  Sensor alignment with Earth-centered Earth-fixed (ECEF) coordinate system , 1999 .

[35]  J. A. Roecker,et al.  Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion , 1988 .