Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar

The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000100 · Cited by 28,164 · see citing works

Robust Model-Based Fault Diagnosis for Dynamic Systems

Chen, J., & Patton, R. J. (1999) · ˜The œKluwer international series on Asian studies in computer and information science · doi:10.1007/978-1-4615-5149-2 · Cited by 4,273 · see citing works

Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations

Fagnant, D. J., & Kockelman, K. (2015) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2015.04.003 · Cited by 3,214 · see citing works

Fault-Diagnosis Systems

Isermann, R. (2005) · Springer eBooks · doi:10.1007/3-540-30368-5 · Cited by 2,217 · see citing works

Potential cyberattacks on automated vehicles

Petit, J., & Shladover, S. E. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2342271 · Cited by 779 · see citing works

Autonomous Vehicle Technology: A Guide for Policymakers

Anderson, J. M., Kalra, N., Stanley, K. D., Sorensen, P. A., Samaras, C., & Oluwatola, O. A. (2014) · RAND Corporation eBooks · doi:10.7249/rr443 · Cited by 590 · see citing works

A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research

Rosique, F., Navarro, P. J., Fernández, C., & Padilla, A. (2019) · Sensors · doi:10.3390/s19030648 · Cited by 469 · see citing works

The impact of adverse weather conditions on autonomous vehicles: How rain, snow, fog, and hail affect the performance of a self-driving car

Zang, S., Ding, M., Smith, D., Tyler, P., Rakotoarivelo, T., & Kaafar, M. A. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2892497 · Cited by 457 · see citing works

Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles

On-Road Automated Driving (ORAD) Committee (2018) · doi:10.4271/j3016_201806 · Cited by 344 · see citing works

Influences of weather phenomena on automotive laser radar systems

Rasshofer, R. H., Spies, M., & Spies, H. F. (2011) · Advances in radio science · doi:10.5194/ars-9-49-2011 · Cited by 317 · see citing works

A Review of Sensor Technologies for Perception in Automated Driving

Martí, E., de Miguel, M. Á., García, F., & Perez, J. (2019) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2019.2907630 · Cited by 315 · see citing works

Lidar for Self-Driving Cars

Hecht, J. (2018) · Optics and Photonics News · doi:10.1364/opn.29.1.000026 · Cited by 282 · see citing works

Illusion and Dazzle: Adversarial Optical Channel Exploits Against Lidars for Automotive Applications

Shin, H., Kim, D., Kwon, Y., & Kim, Y. (2017) · Workshop on Cryptographic Hardware and Embedded Systems · doi:10.1007/978-3-319-66787-4_22 · Cited by 276 · see citing works

Weather Influence and Classification with Automotive Lidar Sensors

Heinzler, R., Schindler, P., Seekircher, J., Ritter, W., & Stork, W. (2019) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2019.8814205 · Cited by 247 · see citing works

A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?

Bijelic, M., Gruber, T., & Ritter, W. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500543 · Cited by 209 · see citing works

Automotive LiDAR performance verification in fog and rain

Kutila, M., Pyykönen, P., Holzhüter, H., Colomb, M., & Duthon, P. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569624 · Cited by 205 · see citing works

Predicting the Influence of Rain on LIDAR in ADAS

Goodin, C. T., Carruth, D. W., Doude, M., & Hudson, C. R. (2019) · Electronics · doi:10.3390/electronics8010089 · Cited by 202 · see citing works

Detecting Attacks Against Robotic Vehicles

Choi, H., Lee, W., Aafer, Y., Fei, F., Tu, Z., Zhang, X., Xu, D., & Deng, X. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243752 · Cited by 184 · see citing works

Quantifying the influence of rain in LiDAR performance

Filgueira, A., González-Jorge, H., Lagüela, S., Díaz-Vilariño, L., & Arias, P. (2016) · Measurement · doi:10.1016/j.measurement.2016.10.009 · Cited by 177 · see citing works

Autonomous Vehicle Security: A Taxonomy of Attacks and Defences

Thing, V. L. L., & Wu, J. (2016) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata.2016.52 · Cited by 177 · see citing works

Automotive LIDAR sensor development scenarios for harsh weather conditions

Kutila, M., Pyykönen, P., Ritter, W., Sawade, O., & Schäufele, B. (2016) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795565 · Cited by 134 · see citing works

Scanning LIDAR in Advanced Driver Assistance Systems and Beyond: Building a road map for next-generation LIDAR technology

Thakur, R. (2016) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2016.2556878 · Cited by 132 · see citing works

Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions

Jokela, M., Kutila, M., & Pyykönen, P. (2019) · Applied Sciences · doi:10.3390/app9112341 · Cited by 125 · see citing works

Automated Driving

Grubmüller, S., Plíhal, J. (., & Nedoma, P. (2016) · Springer eBooks · doi:10.1007/978-3-319-31895-0 · Cited by 123 · see citing works

When the Dust Settles: The Four Behaviors of LiDAR in the Presence of Fine Airborne Particulates

Phillips, T. G., Guenther, N., & McAree, P. R. (2017) · Journal of Field Robotics · doi:10.1002/rob.21701 · Cited by 106 · see citing works

Test methodology for rain influence on automotive surround sensors

Hasirlioglu, S., Kamann, A., Doric, I., & Brandmeier, T. (2016) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795918 · Cited by 99 · see citing works

Automotive LIDAR Technology

Warren, M. E. (2019) · Symposium on VLSI Circuits · doi:10.23919/vlsic.2019.8777993 · Cited by 90 · see citing works

Towards reliable perception for Unmanned Ground Vehicles in challenging conditions

Peynot, T., Underwood, J., & Scheding, S. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354484 · Cited by 87 · see citing works

Field Guide to Lidar

McManamon, P. (2015) · SPIE eBooks · doi:10.1117/3.2186106 · Cited by 77 · see citing works

An efficient outlier removal method for scattered point cloud data

Ning, X., Li, F., Tian, G., & Wang, Y. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201280 · Cited by 77 · see citing works

Identifying the causes of road crashes in Europe.

Thomas, P., Morris, A. P., Talbot, R., & Fagerlind, H. (2013) · Europe PMC (PubMed Central) · Cited by 73 · see citing works

Autonomous Navigation Field Results of a Planetary Analog Robot in Antarctica

Moorehead, S. J., Simmons, G., Apostolopolous, D., & Whittaker, W. (1999) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · Cited by 57 · see citing works

Weather Detection in Vehicles by Means of Camera and LIDAR Systems

Dannheim, C., Icking, C., Mäder, M., & Sallis, P. J. (2014) · International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN) · doi:10.1109/cicsyn.2014.47 · Cited by 54 · see citing works

Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions

Pfeuffer, A., & Dietmayer, K. (2018) · International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455757 · Cited by 54 · see citing works

Introduction to rain and fog attenuation on automotive surround sensors

Hasirlioglu, S., & Riener, A. (2017) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317823 · Cited by 53 · see citing works

Low-THz Radar, Lidar and Optical Imaging through Artificially Generated Fog

Daniel, L., Phippen, D., Hoare, E., Stove, A. G., Cherniakov, M., & Gashinova, M. S. (2017) · IET International Conference on Radar Systems (RADAR) · doi:10.1049/cp.2017.0369 · Cited by 50 · see citing works

RoboADS: Anomaly Detection Against Sensor and Actuator Misbehaviors in Mobile Robots

Guo, P., Kim, H., Virani, N., Xu, J., Zhu, M., & Liu, P. (2018) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2018.00065 · Cited by 50 · see citing works

Reproducible Fog Simulation for Testing Automotive Surround Sensors

Hasirlioglu, S., Doric, I., Kamann, A., & Riener, A. (2017) · IEEE Vehicular Technology Conference · doi:10.1109/vtcspring.2017.8108566 · Cited by 49 · see citing works

Modeling and simulation of rain for the test of automotive sensor systems

Hasirlioglu, S., Doric, I., Lauerer, C., & Brandmeier, T. (2016) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2016.7535399 · Cited by 47 · see citing works

Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer

Piltan, F., & Kim, J.-M. (2018) · Sensors · doi:10.3390/s18041128 · Cited by 43 · see citing works

A Model-Based Approach to Simulate Rain Effects on Automotive Surround Sensor Data

Hasirlioglu, S., & Riener, A. (2018) · International Conference on Intelligent Transportation Systems · doi:10.1109/itsc.2018.8569907 · Cited by 43 · see citing works

A hybrid of industrial maintenance decision making grids

Yunusa‐Kaltungo, A., & Labib, A. (2020) · Production Planning & Control · doi:10.1080/09537287.2020.1741046 · Cited by 43 · see citing works

A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data

Chen, X., Wang, Z., Zhang, Z., Jia, L., & Qin, Y. (2018) · Sensors · doi:10.3390/s18072097 · Cited by 42 · see citing works

Adaptive free space optic system based on visibility detector to overcome atmospheric attenuation

El-Nayal, M. K., Aly, M. H., Fayed, H. A., & Abdelrassoul, R. A. (2019) · Results in Physics · doi:10.1016/j.rinp.2019.102392 · Cited by 41 · see citing works

Effects of exhaust gases on laser scanner data quality at low ambient temperatures

Hasirlioglu, S., Riener, A., Huber, W., & Wintersberger, P. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995954 · Cited by 38 · see citing works

Practical cyber-attacks on autonomous vehicles

Stottelaar, B. (2015) · Cited by 36 · see citing works

The influence of rain on small aperture LiDAR sensors

Fersch, T., Buhmann, A., Koelpin, A., & Weigel, R. (2016) · German Microwave Conference · doi:10.1109/gemic.2016.7461562 · Cited by 36 · see citing works

Mitigation of crosstalk effects in multi-LiDAR configurations

Hebel, M., Hammer, M., Arens, M., & Diehm, A. L. (2018) · Electro-Optical Remote Sensing · doi:10.1117/12.2324305 · Cited by 35 · see citing works

Vehicle identification and GPS error detection from a LIDAR equipped probe vehicle

Gao, B., & Coifman, B. A. (2006) · IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2006.1707442 · Cited by 34 · see citing works

1D MEMS Micro-Scanning LiDAR

Druml, N., Maksymova, I., Thurner, T., van Lierop, D., Hennecke, M. E., & Foroutan, A. (2018) · Cited by 34 · see citing works

An Experiment of Mutual Interference between Automotive LIDAR Scanners

Kim, G., Eom, J., & Park, Y. (2015) · International Conference on Information Technology: New Generations · doi:10.1109/itng.2015.113 · Cited by 33 · see citing works

Analysis of fog and smoke attenuation in a free space optical communication link under controlled laboratory conditions

Ijaz, M., Ghassemlooy, Z. F., Minh, H. L., Rajbhandari, S., & Pérez, J. (2012) · International Workshop on Optical Wireless Communications (IWOW) · doi:10.1109/iwow.2012.6349680 · Cited by 33 · see citing works

Characterization and simulation of the effect of road dirt on the performance of a laser scanner

Rivero, J. R. V., Tahiraj, I., Schubert, O., Glassl, C., Buschardt, B., Berk, M., & Chen, J. (2017) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317784 · Cited by 33 · see citing works

Influence of sensor blockage on automotive LiDAR systems

Trierweiler, M., Caldelas, P., Groninger, G., Peterseim, T., & Neumann, C. (2019) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors43011.2019.8956792 · Cited by 30 · see citing works

Robust LIDAR Localization for Autonomous Driving in Rain

Zhang, C., Ang, M. H., & Rus, D. L. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593703 · Cited by 29 · see citing works

Design of a Fault Detection and Isolation System for Intelligent Vehicle Navigation System

Huang, W., & Su, X. (2015) · International Journal of Navigation and Observation · doi:10.1155/2015/279086 · Cited by 26 · see citing works

Adaptive Sensor Fault Detection and Isolation using Unscented Kalman Filter for Vehicle Positioning

Mori, D., Sugiura, H., & Hattori, Y. (2019) · 2019 IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917208 · Cited by 23 · see citing works

Evaluating Complementary Strengths and Weaknesses of ADAS Sensors

Xique, I. J., Buller, W. T., Fard, Z. B., Dennis, E., & Hart, B. (2018) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2018.8690901 · Cited by 21 · see citing works

Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection.

Periu, C. F., Mohsenimanesh, A. A., Laguë, C. C., & McLaughlin, N. B. (2013) · Canadian Biosystems Engineering · doi:10.7451/cbe.2013.55.2.33 · Cited by 18 · see citing works

Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

Sazara, C., Nezafat, R. V., & Cetin, M. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995813 · Cited by 15 · see citing works

A LiDAR Error Model for Cooperative Driving Simulations

Segata, M., Cigno, R. L., Bhadani, R. K., Bunting, M., & Sprinkle, J. M. (2018) · IEEE Vehicular Networking Conference (VNC) · doi:10.1109/vnc.2018.8628408 · Cited by 15 · see citing works

LiDAR design for road condition measurement ahead of a moving vehicle

Ruiz-Llata, M., Cortina, M. R., Martín‐Mateos, P., Bonilla-Manrique, O. E., & Lopez-Fernandez, J. R. (2017) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/icsens.2017.8234230 · Cited by 14 · see citing works

Performance evaluation of laser scanners through the atmosphere with adverse condition

Hespel, L., Rivière, N., Huet, T., Tanguy, B., & Ceolato, R. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.898010 · Cited by 11 · see citing works

Security of Autonomous Systems under Physical Attacks: With application to Self-Driving Cars

Dutta, R. G. (2018) · Journal of International Crisis and Risk Communication Research · Cited by 10 · see citing works

Spectral attenuation in low visibility artificial fog: Experimental study and comparison to literature models

Mäyrä, A. P., Hietala, E., Kutila, M., & Pyykönen, P. (2017) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2017.8117021 · Cited by 8 · see citing works

Performance evaluation of safety sensors in the indoor fog chamber

Kim, B. K., & Sumi, Y. (2017) · IEEE Underwater Technology (UT) · doi:10.1109/ut.2017.7890317 · Cited by 7 · see citing works

Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather ConditionsPreprint

Pfeuffer, A., & Dietmayer, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.02323 · Cited by 7 · see citing works

High-power 1550 nm tapered DBR laser diodes for LIDAR applications

Aho, A. T., Viheriälä, J., Mäkelä, J., Virtanen, H. T., Ranta, S., Dumitrescu, M. M., & Guina, M. D. (2017) · Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) · doi:10.1109/cleoe-eqec.2017.8086373 · Cited by 6 · see citing works

Anti-dynamic-crosstalk method for single photon LIDAR detection

Zhang, F., Fu, X., Liu, Q., & Gong, M. (2017) · LIDAR Imaging Detection and Target Recognition · doi:10.1117/12.2286068 · Cited by 3 · see citing works

Experimental study on spectral absorbance in fog as a function of temperature, liquid water content, and particle size

Mäyrä, A. P., Hietala, E., Kutila, M., Pyykönen, P., Tiihonen, M., & Jokela, T. (2017) · Environmental Effects on Light Propagation and Adaptive Systems · doi:10.1117/12.2277308 · Cited by 2 · see citing works

Basic Experiment of LIDAR Sensor Measurement Directional Instability for Moving and Vibrating Object

Hama, S., & Toda, H. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/472/1/012017 · Cited by 2 · see citing works

A Review on Fault Detection and Diagnosis of Industrial Robots and Multi-axis Machines

Sabry, A. H., & Amirulddin, U. A. U. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102397 · Cited by 93 · see citing works

A review on digital twin application in photovoltaic energy systems: challenges and opportunities

Alao, K. T., Gilani, S. I. U.-H., Sopian, K., & Alao, T. O. (2024) · JMST Advances · doi:10.1007/s42791-024-00083-z · Cited by 56 · see citing works

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

Gomes, T., Matias, D., Campos, A., Cunha, L., & Roriz, R. (2023) · Sensors · doi:10.3390/s23020601 · Cited by 54 · see citing works

Visibility of various road markings for machine vision

Burghardt, T. E., Popp, R., Helmreich, B., Reiter, T., Böhm, G., Pitterle, G., & Artmann, M. (2021) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2021.e00579 · Cited by 53 · see citing works

Safe Autonomous Driving in Adverse Weather: Sensor Evaluation and Performance Monitoring

Sezgin, F., Vriesman, D., Steinhauser, D., Lugner, R., & Brandmeier, T. (2023) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55152.2023.10186596 · Cited by 50 · see citing works

Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors

Linnhoff, C., Hofrichter, K., Elster, L., Rosenberger, P., & Winner, H. (2022) · Sensors · doi:10.3390/s22145266 · Cited by 44 · see citing works

Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey

Raouf, I., Khan, A., Khalid, S., Sohail, M., Azad, M. M., & Kim, H. S. (2022) · Mathematics · doi:10.3390/math10183233 · Cited by 41 · see citing works

A Review on Driving Control Issues for Smart Electric Vehicles

Haque, T. S., Rahman, M. H., Islam, M. R., Razzak, M. A., Badal, F. R., Ahamed, M. H., Moyeen, S. I., Das, S. K., Ali, M. F., Tasneem, Z., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3116353 · Cited by 37 · see citing works

Lightweight Fault Detection Strategy for Wireless Sensor Networks Based on Trend Correlation

Fu, X., Wang, Y., Li, W., Yang, Y., & Postolache, O. A. (2021) · IEEE Access · doi:10.1109/access.2021.3049837 · Cited by 30 · see citing works

Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit, and Effects on the Point Cloud

Schlager, B., Goelles, T., Behmer, M., Muckenhuber, S., Payer, J., & Watzenig, D. (2022) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2022.3176471 · Cited by 30 · see citing works

Self-Detection and Self-Diagnosis Methods for Sensors in Intelligent Integrated Sensing System

Zhu, M., Li, J., Wang, W., & Chen, D. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3090990 · Cited by 29 · see citing works

Contaminations on Lidar Sensor Covers: Performance Degradation Including Fault Detection and Modeling as Potential Applications

Schlager, B., Goelles, T., Muckenhuber, S., & Watzenig, D. (2022) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2022.3214094 · Cited by 28 · see citing works

Spatial Environment Perception and Sensing in Automated Systems: A Review

Fei, T., Mukhopadhyay, S. C., da Costa, J. P. C. L., Roychaudhuri, C., Lan, L., & Demitri, N. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3379222 · Cited by 27 · see citing works

Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion

Hou, W., Li, W., & Li, P. (2023) · Sensors · doi:10.3390/s23115110 · Cited by 27 · see citing works

Diagnosing and Balancing Approaches of Bowed Rotating Systems: A Review

Rezazadeh, N., Luca, A. D., Lamanna, G., & Caputo, F. (2022) · Applied Sciences · doi:10.3390/app12189157 · Cited by 22 · see citing works

Decentralized fault-tolerant control of multi-mobile robot system addressing LiDAR sensor faults

Elsayed, A. M., Elshalakani, M., Hammad, S. A., & Maged, S. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-75500-3 · Cited by 17 · see citing works

The Use of eXplainable Artificial Intelligence and Machine Learning Operation Principles to Support the Continuous Development of Machine Learning-Based Solutions in Fault Detection and Identification

Tran, T.-A., Ruppert, T., & Abonyi, J. (2024) · Computers · doi:10.3390/computers13100252 · Cited by 16 · see citing works

What are the most influential factors in a vehicle platoon?

Monasterio, F. V., Useros, M. M., Jiménez‐Salas, M., Boada, B. L., & Boada, M. J. L. (2024) · IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) · doi:10.1109/eais58494.2024.10569102 · Cited by 15 · see citing works

Cross-modal Matching CNN for Autonomous Driving Sensor Data Monitoring

Chen, Y., Liu, F., & Pei, K. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00346 · Cited by 15 · see citing works

Fault detection with confidence level evaluation for perception module of autonomous vehicles based on long short term memory and Gaussian Mixture Model

Jeong, Y. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111010 · Cited by 15 · see citing works

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

Kowalczyk, P., Komorkiewicz, M., Skruch, P., & Szelest, M. (2022) · IEEE Access · doi:10.1109/access.2022.3145192 · Cited by 13 · see citing works

When Is It Likely to Fail? Performance Monitor for Black-Box Trajectory Prediction Model

Shao, W., Li, B., Yu, W., Xu, J., & Wang, H. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3356070 · Cited by 10 · see citing works

Fault diagnosis based on algebraic identification assisted by extended state observers

Rojas-Cubides, H., Rojas, H. E., & Cortes-Romero, J. A. (2021) · Nonlinear Dynamics · doi:10.1007/s11071-021-07053-3 · Cited by 10 · see citing works

Effects of Sensor Cover Damages on Point Clouds of Automotive Lidar

Schlager, B., Goelles, T., & Watzenig, D. (2021) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors47087.2021.9639697 · Cited by 10 · see citing works

Predicting the Influence of Adverse Weather on Pedestrian Detection with Automotive Radar and Lidar Sensors

Weihmayr, D., Sezgin, F., Tolksdorf, L., Birkner, C., & Jazar, R. N. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588472 · Cited by 8 · see citing works

Correlating Intrinsic Parameters and Sharpness for Condition Monitoring of Automotive Imaging Sensors

Kettelgerdes, M., Bohm, L., & Elger, G. (2021) · International Conference on System Reliability and Safety (ICSRS) · doi:10.1109/icsrs53853.2021.9660665 · Cited by 8 · see citing works

TinyLid: a RISC-V accelerated Neural Network For LiDAR Contaminant Classification in Autonomous Vehicle

Jati, G., Molan, M., Barchi, F., Bartolini, A., & Acquaviva, A. (2024) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/3649153.3649201 · Cited by 7 · see citing works

Mutual Information-Guided Domain-Shared Feature Learning for Bearing Fault Diagnosis Under Unknown Conditions

Xu, K., Li, S., Xu, S., Hu, Y., Mao, Y., & Chai, Y. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3552003 · Cited by 7 · see citing works

Considerations for Determining the Coefficient of Inertia Masses for a Tracked Vehicle

Alexa, O., Coropeţchi, I. C., Vasile, A., Oncioiu, I., & Grigore, L. Ș. (2020) · Sensors · doi:10.3390/s20195587 · Cited by 7 · see citing works

Contrast Ratio of Road Markings in Poland - Evaluation for Machine Vision Applications Based on Naturalistic Driving Study

Burghardt, T. E., & Pashkevich, A. (2022) · Sustainable civil infrastructures · doi:10.1007/978-3-030-79801-7_49 · Cited by 7 · see citing works

Image quality safety model for the safety of the intended functionality in highly automated agricultural machines

Lee, C., Schätzle, S., Lang, S. A., & Oksanen, T. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109622 · Cited by 6 · see citing works

Working Condition-Decoupled and Invariant-Feature Fusion Transformer for Domain Generalization Intelligent Fault Diagnosis

Xu, K., Li, H., Lu, M., Chai, Y., Hu, Y., Wang, S., & Zhang, K. (2026) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2026.3661501 · Cited by 5 · see citing works

Failure Mode Investigation to Enable LiDAR Health Monitoring for Automotive Application

Chang, F., Jafarzadeh, E., Gatto, J. D., Cran, G., & Sadjadi, H. (2023) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2023.v15i1.3526 · Cited by 5 · see citing works

Aircraft flight control system fault tolerance under structural and parametric uncertainties

Kosyanchuk, V., Zheltov, S. Y., & Zybin, E. Y. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1864/1/012005 · Cited by 5 · see citing works

Data‐driven modelling for online fault pre‐warning in thermal power plant using incremental Gaussian mixture regression

Jin, S., Si, F., Dong, Y., & Ren, S. (2023) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.25133 · Cited by 5 · see citing works

Engineering systems fault detection methods

de Souza, G. F. M., Netto, A. C., Melani, A. H. D. A., Michalski, M. A. D. C., & da Silva, R. F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823521-8.00003-7 · Cited by 5 · see citing works

Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors

Kettelgerdes, M., Sarmiento, N., Erdogan, H., Wunderle, B., & Elger, G. (2024) · Remote Sensing · doi:10.3390/rs16132407 · Cited by 4 · see citing works

Radar Fault Detection via Camera-Radar Branches Learning Network

Ning, D., & Han, D. S. (2023) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic57133.2023.10067071 · Cited by 3 · see citing works

A Generic Risk Assessment Methodology and its Implementation as a Run-time Monitoring Device for Automated Vehicles

Tong, K., Solmaz, S., Šikić, H., & Reckenzaun, J. (2023) · Transportation research procedia · doi:10.1016/j.trpro.2023.11.408 · Cited by 3 · see citing works

Fault detection, isolation, identification and recovery methods for perception systems in automated railway transport: A review

Renatus, K., Rabe, M. D., Bäker, B., Waschulzik, T., & Schories, K. (2026) · Transportation Engineering · doi:10.1016/j.treng.2026.100421 · Cited by 2 · see citing works

Residual Risk Management Strategies at System Level presented for ACC/LKA Behavioural Competencies

Solmaz, S., Stettinger, G., & Wotawa, F. (2023) · IEEE International Automated Vehicle Validation Conference (IAVVC) · doi:10.1109/iavvc57316.2023.10328110 · Cited by 2 · see citing works

Effects of Sensor Cover Damages on Point Clouds of Automotive LidarPreprint

Schlager, B., Goelles, T., & Watzenig, D. (2022) · doi:10.36227/techrxiv.18101279.v1 · Cited by 2 · see citing works

TinyML Anomaly Detection and Fault Prediction for Industrial ApplicationsPreprint

Samuel, E. T., Polonelli, T., Magno, M., & Esan, A. (2025) · Research Square · doi:10.21203/rs.3.rs-8379288/v1 · Cited by 2 · see citing works

Failure prediction and uncertainty verification of LiDAR thermoelectric coolers using Archimedes spiral loss-based domain generalization

Lee, J., Lee, S., & Lee, J. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111942 · Cited by 1 · see citing works

Fusion-Based Deep Learning Techniques for Fault Isolation in Connected Vehicles

Mousavi, M., Kordestani, M., Moradi, M., Chaibakhsh, A., & Saif, M. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3649740 · Cited by 1 · see citing works

Precise intrinsic calibration and geometrical stability analysis of automotive solid-state LiDAR sensors over temperature and lifetime

Kettelgerdes, M., Kooijmans, E. L., Erdogan, H., Wunderle, B., & Elger, G. (2026) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2026.109761 · Cited by 1 · see citing works

Implementation of the Correction Algorithm in an Environment with Dynamic Actors

Omwansa, M., & Meyer, R. (2023) · SAE International Journal of Connected and Automated Vehicles · doi:10.4271/12-06-03-0021 · Cited by 1 · see citing works

Enhancing Seismic Data Accuracy: An Advanced Health Diagnosis Method for Seismometers Performance Evaluation

Annisa, R., Jannah, M., Saputro, A. H., & Lestari, T. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2866/1/012049 · Cited by 1 · see citing works

Trends in Sensors Fault Diagnosis

Witczak, P. (2021) · Sensors · doi:10.3390/s21062224

Effects of Sensor Cover Damages on Point Clouds of Automotive LidarPreprint

Schlager, B., Goelles, T., & Watzenig, D. (2022) · doi:10.36227/techrxiv.18101279

Causal-Structural Dependency Graphs for Automated Diagnostic Validation in Vehicles

Rabe, M. D., Schories, K., Bäker, B., & Unger, A. (2023) · Proceedings · doi:10.1007/978-3-658-42048-2_34

Robust perception systems for automated, connected, and electrified vehicles: advances from EU project ArchitectECA2030

Reckenzaun, J., Solmaz, S., Goelles, T., Hilbert, M., Weimer, D., Mayer, P. B., Chromý, A., Hentschel, U., Modler, N., Tóth, M., et al. (2023) · Transportation research procedia · doi:10.1016/j.trpro.2023.11.412

Mobile robot replacement in multi-robot fault-tolerant formation

Elsayed, A. M., Elshalakani, M., Hammad, S. A., & Maged, S. A. (2025) · IAES International Journal of Robotics and Automation (IJRA) · doi:10.11591/ijra.v14i3.pp311-319

Seeing More With Less: Leveraging Positional Telemetry for V2X Cooperative Perception

Chen, S., Sabnani, I., Hadžić, I., Khan, M. A., Dahbura, A. T., & Sabnani, K. K. (2025) · IEEE Future Networks World Forum (FNWF) · doi:10.1109/fnwf66845.2025.11317191

Key algorithms for fault diagnosis and prevention in automated in-flight catering heating systems

Lakhtin, A. S., & Goncharov, A. (2025) · Russian journal of resources conservation and recycling · doi:10.15862/06faor225

Functional Safety Modeling and Design for LiDAR in Autonomous Driving

Zhao, X., Li, X., Yu, Q., Li, C., Jiang, K., Sun, K., & Yang, D. (2025) · CAA International Conference on Vehicular Control and Intelligence (CVCI) · doi:10.1109/cvci66304.2025.11348286

Lidar-based snow monitoring from aerial lifts: Gondola deployment in the Austrian Alps

Đikić, B., Goelles, T., Gaisberger, C., Schlager, B., Muckenhuber, S., Batista, P., Keuschnig, M., & Schratter, M. (2026) · Journal of Glaciology · doi:10.1017/jog.2025.10105

Fault tolerant decentralized collaboration for simultaneous localization and prior map update with stable 2D features

Escourrou, M., Hage, J. A., & Bonnifait, P. (2026) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2026.105402

Resilient Vehicle Navigation Utilizing Decentralized Integration of Solid-State LiDARs and 3-D Digital Maps

Elsayed, M. A., Mounier, E., & Noureldin, A. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3675951

Hybrid 3D Graph Convolutional Network and Transformer for Lidar Fault Detection

Seong, J. H., KIM, J., Lee, S. Y., Kim, T. H., Kim, B. S., & Yun, K.-S. (2026) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic68212.2026.11454417

Collaborative Visual Localization for Modular Self‐Reconfigurable Robots

Liang, G., Ou, R., Wang, P., Wang, X., & Lam, T. L. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.202501291

Information Preprocessing for Multimodal Collaborative Perception

Wang, B. (2026) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-92-0052-8_3

Real-time Sensor Diagnosis and Behavior Tree-based Fault Handling Framework using ROS2 CDR Data Parsing

Eum, T.-W., Choi, J. H., An, Y. C., & Kuc, T. (2026) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur69537.2026.11626748

Tri-dimensional entropy reduction for perception uncertainty mitigation and system-level safety enhancement in autonomous driving

Zhao, X., Jiang, K., Yang, M., & Yang, D. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113263

Safety Engineering of Human-Robot Collaboration Tasks Using a 3D LiDAR

Giamberini, G., Katranis, G., Zoppi, T., Atif, M., Lollini, P., Ceccarelli, A., Morozov, A. S., & Bondavalli, A. (2026) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa68377.2026.11725110

Contaminations on Lidar Sensor Covers: Performance Degradation Including Fault Detection and Modeling as Potential Applications

Schlager, B., Goelles, T., Muckenhuber, S., & Watzenig, D. (2022) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2022.3214094 · Cited by 28 · see citing works

Effects of Sensor Cover Damages on Point Clouds of Automotive Lidar

Schlager, B., Goelles, T., & Watzenig, D. (2021) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors47087.2021.9639697 · Cited by 10 · see citing works

Fault Detection and Recovery for Automotive Perception Sensors Based on AI and Regarding Driver Comfort

Driving Simulation Association, Scheffmann, M., Salah, A., & Reuss, H.-C. (2025) · DSA proceedings · doi:10.82157/dsa/2024/18

Radar, Vision, Lidar Fusion-based Environment Sensor Fault Detection Algorithm for Automated Vehicles

최승리, 정용환, 이명수, & 이경수 (2017) · Cited by 1 · see citing works

Fault analysis of vehicle mounted LiDAR

Li, R., & Liu, X. (2024) · International Conference on Autonomous Driving and Intelligent Sensing Technology (ADIST) · doi:10.1145/3696021.3696032