{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:44:07Z","timestamp":1760060647782,"version":"build-2065373602"},"reference-count":108,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Autonomous vehicles (AVs) are increasingly becoming a reality, enabled by advances in sensing technologies, intelligent control systems, and real-time data processing. For AVs to operate safely and effectively, they must maintain a reliable perception of their surroundings and internal state. However, sensor failures, whether due to noise, malfunction, or degradation, can compromise this perception and lead to incorrect localization or unsafe decisions by the autonomous control system. While modern AV systems often combine data from multiple sensors to mitigate such risks through sensor fusion techniques (e.g., Kalman filtering), the extent to which these systems remain resilient under faulty conditions remains an open question. This work presents a simulation-based fault injection framework to assess the impact of sensor failures on AVs\u2019 behavior. The framework enables structured testing of autonomous driving software under controlled fault conditions, allowing researchers to observe how specific sensor failures affect system performance. To demonstrate its applicability, an experimental campaign was conducted using the CARLA simulator integrated with the Autoware autonomous driving stack. A multi-segment urban driving scenario was executed using a modified version of CARLA\u2019s Scenario Runner to support Autoware-based evaluations. Faults were injected simulating LiDAR, GNSS, and IMU sensor failures in different route scenarios. The fault types considered in this study include silent sensor failures and severe noise. The results obtained by emulating sensor failures in our chosen system under test, Autoware, show that faults in LiDAR and IMU gyroscope have the most critical impact, often leading to erratic motion and collisions. In contrast, faults in GNSS and IMU accelerometers were well tolerated. This demonstrates the ability of the framework to investigate the fault-tolerance of AVs in the presence of critical sensor failures.<\/jats:p>","DOI":"10.3390\/informatics12030094","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T08:56:46Z","timestamp":1758013006000},"page":"94","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Simulating the Effects of Sensor Failures on Autonomous Vehicles for Safety Evaluation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2305-6409","authenticated-orcid":false,"given":"Francisco","family":"Matos","sequence":"first","affiliation":[{"name":"Polytechnic University of Coimbra, Rua da Miseric\u00f3rdia, Lagar dos Corti\u00e7os, S. Martinho do Bispo, 3045-093 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9697-9991","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Dur\u00e3es","sequence":"additional","affiliation":[{"name":"Polytechnic University of Coimbra, Rua da Miseric\u00f3rdia, Lagar dos Corti\u00e7os, S. Martinho do Bispo, 3045-093 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4777-0315","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Cunha","sequence":"additional","affiliation":[{"name":"Polytechnic University of Coimbra, Rua da Miseric\u00f3rdia, Lagar dos Corti\u00e7os, S. Martinho do Bispo, 3045-093 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"154615","DOI":"10.1016\/j.scitotenv.2022.154615","article-title":"Environmental impacts of autonomous vehicles: A review of the scientific literature","volume":"830","author":"Silva","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Luca, O., Andrei, L., Iacoboaea, C., and Gaman, F. (2023). Unveiling the Hidden Effects of Automated Vehicles on \u201cDo No Significant Harm\u2019\u2019 Components. Sustainability, 15.","DOI":"10.3390\/su151411265"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ahangar, M.N., Ahmed, Q.Z., Khan, F.A., and Hafeez, M. (2021). A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges. Sensors, 21.","DOI":"10.3390\/s21030706"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11097","DOI":"10.1109\/JSEN.2023.3262134","article-title":"Sensing and Machine Learning for Automotive Perception: A Review","volume":"23","author":"Pandharipande","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jha, S., Banerjee, S.S., Cyriac, J., Kalbarczyk, Z.T., and Iyer, R.K. (2018, January 25\u201328). AVFI: Fault Injection for Autonomous Vehicles. Proceedings of the 48th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018, Luxembourg.","DOI":"10.1109\/DSN-W.2018.00027"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maleki, M., Farooqui, A., and Sangchoolie, B. (2023, January 27\u201330). CarFASE: A Carla-based Tool for Evaluating the Effects of Faults and Attacks on Autonomous Driving Stacks. Proceedings of the 53rd Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2023, Porto, Portugal.","DOI":"10.1109\/DSN-W58399.2023.00036"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jha, S., Banerjee, S., Tsai, T., Hari, S.K.S., Sullivan, M.B., Kalbarczyk, Z.T., Keckler, S.W., and Iyer, R.K. (2019, January 24\u201327). ML-Based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection. Proceedings of the 2019 49th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN), Portland, OR, USA.","DOI":"10.1109\/DSN.2019.00025"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Maleki, M., and Sangchoolie, B. (2021, January 13\u201316). SUFI: A Simulation-based Fault Injection Tool for Safety Evaluation of Advanced Driver Assistance Systems Modelled in SUMO. Proceedings of the 2021 17th European Dependable Computing Conference, EDCC 2021, Munich, Germany.","DOI":"10.1109\/EDCC53658.2021.00014"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.ifacol.2019.08.077","article-title":"MOBATSim: MOdel-Based Autonomous Traffic Simulation Framework for Fault-Error-Failure Chain Analysis","volume":"52","author":"Saraoglu","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gosavi, M.A., Rhoades, B.B., and Conrad, J.M. (2018, January 19\u201322). Application of Functional Safety in Autonomous Vehicles Using ISO 26262 Standard: A Survey. Proceedings of the IEEE SOUTHEASTCON 2018, St. Petersburg, FL, USA.","DOI":"10.1109\/SECON.2018.8479057"},{"key":"ref_11","first-page":"210","article-title":"On Three-Layer Architectures","volume":"195","author":"Gat","year":"1998","journal-title":"Artif. Intell. Mob. Robot."},{"key":"ref_12","unstructured":"(2025, August 14). Subsumption Control of a Mobile Robot. Available online: https:\/\/www.researchgate.net\/publication\/2875073_Subsumption_Control_of_a_Mobile_Robot."},{"key":"ref_13","unstructured":"(2025, August 14). The 3T Intelligent Control Architecture|Download Scientific Diagram. Available online: https:\/\/www.researchgate.net\/figure\/The-3T-Intelligent-Control-Architecture_fig1_2851637."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104096","DOI":"10.1016\/j.robot.2022.104096","article-title":"A survey of Behavior Trees in robotics and AI","volume":"154","author":"Iovino","year":"2022","journal-title":"Rob. Auton. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/0005-1098(89)90002-2","article-title":"Model predictive control: Theory and practice\u2014A survey","volume":"25","author":"Prett","year":"1989","journal-title":"Automatica"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pendleton, S.D., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y.H., Rus, D., and Ang, M.H. (2017). Perception, Planning, Control, and Coordination for Autonomous Vehicles. Machines, 5.","DOI":"10.3390\/machines5010006"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Velasco-Hernandez, G., Yeong, D.J., Barry, J., and Walsh, J. (2020, January 3\u20135). Autonomous Driving Architectures, Perception and Data Fusion: A Review. Proceedings of the 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP51029.2020.9266268"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115865","DOI":"10.1109\/ACCESS.2023.3326069","article-title":"A Survey on Localization for Autonomous Vehicles","volume":"11","author":"Kumar","year":"2023","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1109\/TIV.2022.3192102","article-title":"A Survey on Map-Based Localization Techniques for Autonomous Vehicles","volume":"8","author":"Chalvatzaras","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"16962","DOI":"10.1109\/TITS.2022.3156011","article-title":"Scenario Understanding and Motion Prediction for Autonomous Vehicles\u2014Review and Comparison","volume":"23","author":"Karle","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_21","unstructured":"Wang, X., Maleki, M.A., Azhar, M.W., and Trancoso, P. (2024). Moving Forward: A Review of Autonomous Driving Software and Hardware Systems. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, Z., Nie, L., Yin, Z., and Huang, S. (2020). A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles. Sensors, 20.","DOI":"10.3390\/s20133689"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.1109\/TIV.2023.3235007","article-title":"Dynamic Drifting Control for General Path Tracking of Autonomous Vehicles","volume":"8","author":"Chen","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yeong, D.J., Velasco-Hernandez, G., Barry, J., and Walsh, J. (2021). Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review. Sensors, 21.","DOI":"10.20944\/preprints202102.0459.v1"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.procs.2021.12.315","article-title":"An overview of sensors in Autonomous Vehicles","volume":"198","author":"Ignatious","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Matos, F., Bernardino, J., Dur\u00e3es, J., and Cunha, J. (2024). A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions. Sensors, 24.","DOI":"10.3390\/s24165108"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1109\/TIV.2022.3182218","article-title":"Applications and Services Using Vehicular Exteroceptive Sensors: A Survey","volume":"8","author":"Ortiz","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Budisusila, E.N., Khosyi\u2019in, M., Prasetyowati, S.A.D., Suprapto, B.Y., and Nawawi, Z. (2021, January 20\u201321). Ultrasonic Multi-Sensor Detection Patterns on Autonomous Vehicles Using Data Stream Method. Proceedings of the 2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Semarang, Indonesia.","DOI":"10.23919\/EECSI53397.2021.9624313"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1049\/iet-its.2017.0406","article-title":"Smart parking sensors, technologies and applications for open parking lots: A review","volume":"12","author":"Paidi","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Vargas, J., Alsweiss, S., Toker, O., Razdan, R., and Santos, J. (2021). An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions. Sensors, 21.","DOI":"10.3390\/s21165397"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rosique, F., Navarro, P.J., Fern\u00e1ndez, C., and Padilla, A. (2019). A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research. Sensors, 19.","DOI":"10.3390\/s19030648"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1109\/ACCESS.2019.2962554","article-title":"Multi-Sensor Fusion in Automated Driving: A Survey","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1038\/s41467-019-09380-x","article-title":"Partially coherent radar unties range resolution from bandwidth limitations","volume":"10","author":"Komissarov","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Skaria, S., Al-Hourani, A., Evans, R.J., Sithamparanathan, K., and Parampalli, U. (2019). Interference Mitigation in Automotive Radars Using Pseudo-Random Cyclic Orthogonal Sequences. Sensors, 19.","DOI":"10.3390\/s19204459"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1049\/rsn2.12132","article-title":"Automotive interference statistics and their effect on radar detector","volume":"16","author":"Pirkani","year":"2022","journal-title":"IET Radar Sonar Navig."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wu, Z., Song, Y., Liu, J., Chen, Y., Sha, H., Shi, M., Zhang, H., Qin, L., Liang, L., and Jia, P. (2024). Advancements in Key Parameters of Frequency-Modulated Continuous-Wave Light Detection and Ranging: A Research Review. Appl. Sci., 14.","DOI":"10.3390\/app14177810"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7332","DOI":"10.1364\/OE.508004","article-title":"Frequency modulated continuous wave and time of flight LIDAR with single photons: A comparison","volume":"32","author":"Staffas","year":"2024","journal-title":"Opt. Express"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101201","DOI":"10.1088\/1674-4926\/24040015","article-title":"A review of ToF-based LiDAR","volume":"45","author":"Ma","year":"2024","journal-title":"J. Semicond."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2100511","DOI":"10.1002\/lpor.202100511","article-title":"A Progress Review on Solid-State LiDAR and Nanophotonics-Based LiDAR Sensors","volume":"16","author":"Li","year":"2022","journal-title":"Laser Photon Rev."},{"key":"ref_40","first-page":"031213","article-title":"Technical concepts of automotive LiDAR sensors: A review","volume":"62","author":"Bayesteh","year":"2023","journal-title":"Opt. Eng."},{"key":"ref_41","first-page":"50","article-title":"Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dreissig, M., Scheuble, D., Piewak, F., and Boedecker, J. (2023, January 4\u20137). Survey on LiDAR Perception in Adverse Weather Conditions. Proceedings of the 2023 IEEE Intelligent Vehicles Symposium (IV), Anchorage, AK, USA.","DOI":"10.1109\/IV55152.2023.10186539"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Damodaran, D., Mozaffari, S., Alirezaee, S., and Ahamed, M.J. (2023). Experimental Analysis of the Behavior of Mirror-like Objects in LiDAR-Based Robot Navigation. Appl. Sci., 13.","DOI":"10.3390\/app13052908"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Roszyk, K., Nowicki, M.R., and Skrzypczy\u0144ski, P. (2022). Adopting the YOLOv4 Architecture for Low-Latency Multispectral Pedestrian Detection in Autonomous Driving. Sensors, 22.","DOI":"10.3390\/s22031082"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sun, C., Chen, Y., Qiu, X., Li, R., and You, L. (2024). MRD-YOLO: A Multispectral Object Detection Algorithm for Complex Road Scenes. Sensors, 24.","DOI":"10.3390\/s24103222"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1109\/TCDS.2023.3238181","article-title":"YOLO-MS: Multispectral Object Detection via Feature Interaction and Self-Attention Guided Fusion","volume":"15","author":"Xie","year":"2023","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"11489","DOI":"10.1109\/JSEN.2022.3172386","article-title":"The Use of Thermal Cameras for Pedestrian Detection","volume":"22","author":"Altay","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1109\/TDSC.2022.3156941","article-title":"RGB Cameras Failures and Their Effects in Autonomous Driving Applications","volume":"20","author":"Ceccarelli","year":"2023","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"108336","DOI":"10.1016\/j.measurement.2020.108336","article-title":"Determination of GNSS receiver elevation-dependent clock bias accuracy","volume":"168","author":"Maciuk","year":"2021","journal-title":"Measurement"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Raveena, C.S., Sravya, R.S., Kumar, R.V., and Chavan, A. (2020, January 6\u20138). Sensor Fusion Module Using IMU and GPS Sensors for Autonomous Car. Proceedings of the 2020 IEEE International Conference for Innovation in Technology (INOCON), Bangluru, India.","DOI":"10.1109\/INOCON50539.2020.9298316"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2893","DOI":"10.1109\/TIV.2023.3316361","article-title":"A Generalizable D-VIO and Its Fusion with GNSS\/IMU for Improved Autonomous Vehicle Localization","volume":"9","author":"Yusefi","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"14845","DOI":"10.1109\/JIOT.2021.3072354","article-title":"Autonomous Vehicles Sideslip Angle Estimation: Single Antenna GNSS\/IMU Fusion With Observability Analysis","volume":"8","author":"Xia","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Shahian Jahromi, B., Tulabandhula, T., and Cetin, S. (2019). Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles. Sensors, 19.","DOI":"10.3390\/s19204357"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Nobis, F., Geisslinger, M., Weber, M., Betz, J., and Lienkamp, M. (2019, January 15\u201317). A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection. Proceedings of the 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF), Bonn, Germany.","DOI":"10.1109\/SDF.2019.8916629"},{"key":"ref_55","unstructured":"Intellias (2025, June 14). How Sensor Fusion for Autonomous Cars Helps Avoid Deaths on the Road. Intellias Blog. Available online: https:\/\/intellias.com\/sensor-fusion-autonomous-cars-helps-avoid-deaths-road\/."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Brena, R.F., Aguileta, A.A., Trejo, L.A., Molino-Minero-Re, E., and Mayora, O. (2020). Choosing the Best Sensor Fusion Method: A Machine-Learning Approach. Sensors, 20.","DOI":"10.3390\/s20082350"},{"key":"ref_57","first-page":"36","article-title":"Multi-Sensor Fusion and Cooperative Perception for Autonomous Driving: A Review","volume":"15","author":"Xiang","year":"2023","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Fayyad, J., Jaradat, M.A., Gruyer, D., and Najjaran, H. (2020). Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review. Sensors, 20.","DOI":"10.3390\/s20154220"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Gu, S., Zhang, Y., Yang, J., Alvarez, J.M., and Kong, H. (2019, January 4\u20138). Two-View Fusion based Convolutional Neural Network for Urban Road Detection. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968054"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"25107","DOI":"10.1007\/s11042-023-14417-x","article-title":"Fully convolutional neural networks for LIDAR\u2013camera fusion for pedestrian detection in autonomous vehicle","volume":"82","author":"Muthu","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"110862","DOI":"10.1016\/j.ymssp.2023.110862","article-title":"GNSS\/IMU\/LiDAR fusion for vehicle localization in urban driving environments within a consensus framework","volume":"205","author":"Gao","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, J., and Cho, J. (2019, January 16\u201318). An advanced object classification strategy using YOLO through camera and LiDAR sensor fusion. Proceedings of the 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), Gold Coast, Australia.","DOI":"10.1109\/ICSPCS47537.2019.9008742"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Banerjee, K., Notz, D., Windelen, J., Gavarraju, S., and He, M. (2018, January 26\u201330). Online Camera LiDAR Fusion and Object Detection on Hybrid Data for Autonomous Driving. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500699"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Pollach, M., Schiegg, F., and Knoll, A. (August, January 31). Low Latency and Low-Level Sensor Fusion for Automotive Use-Cases. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196717"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1109\/TITS.2023.3317372","article-title":"Multi-Sensor Fusion Technology for 3D Object Detection in Autonomous Driving: A Review","volume":"25","author":"Wang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"4901","DOI":"10.1109\/JSEN.2020.2966034","article-title":"Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications","volume":"20","author":"Zhao","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Bijelic, M., Gruber, T., Mannan, F., Kraus, F., Ritter, W., Dietmayer, K., and Heide, F. (2020, January 13\u201319). Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01170"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"AlZu\u2019bi, S., and Jararweh, Y. (July, January 30). Data Fusion in Autonomous Vehicles Research, Literature Tracing from Imaginary Idea to Smart Surrounding Community. Proceedings of the 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC), Paris, France.","DOI":"10.1109\/FMEC49853.2020.9144916"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Hasanujjaman, M., Chowdhury, M.Z., and Jang, Y.M. (2023). Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking. Sensors, 23.","DOI":"10.3390\/s23063335"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ogunrinde, I., and Bernadin, S. (2023). Deep Camera\u2013Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions. Sensors, 23.","DOI":"10.20944\/preprints202305.2180.v1"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/TIV.2023.3307157","article-title":"Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review","volume":"9","author":"Yao","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Choi, J.D., and Kim, M.Y. (2021, January 17\u201320). A Sensor Fusion System with Thermal Infrared Camera and LiDAR for Autonomous Vehicles: Its Calibration and Application. Proceedings of the 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN), Jeju, Republic of Korea.","DOI":"10.1109\/ICUFN49451.2021.9528609"},{"key":"ref_73","first-page":"1","article-title":"End-to-End Target Liveness Detection via mmWave Radar and Vision Fusion for Autonomous Vehicles","volume":"20","author":"Wang","year":"2024","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Shi, J., Tang, Y., Gao, J., Piao, C., and Wang, Z. (2023). Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous Vehicles. Sensors, 23.","DOI":"10.3390\/s23156920"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Lai, Z., Le, L., Silva, V., and Br\u00e4unl, T. (2025, June 16). A Comprehensive Comparative Analysis of Carla and Awsim: Open-Source Autonomous Driving Simulators. Available online: https:\/\/ssrn.com\/abstract=5096777.","DOI":"10.2139\/ssrn.5096777"},{"key":"ref_76","unstructured":"(2025, June 16). Autoware Universe Documentation. Available online: https:\/\/autowarefoundation.github.io\/autoware_universe\/main\/."},{"key":"ref_77","unstructured":"(2025, July 12). Autoware Overview\u2014Autoware. Available online: https:\/\/autoware.org\/autoware-overview\/."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1109\/32.44380","article-title":"Fault Injection for Dependability Validation: A Methodology and Some Applications","volume":"16","author":"Arlat","year":"1990","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hong, D., and Moon, C. (2024). Autonomous Driving System Architecture with Integrated ROS2 and Adaptive AUTOSAR. Electronics, 13.","DOI":"10.3390\/electronics13071303"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Rong, G., Shin, B.H., Tabatabaee, H., Lu, Q., Lemke, S., Mo\u017eeiko, M., Boise, E., Uhm, G., Gerow, M., and Mehta, S. (2020, January 20\u201323). LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020, Rhodes, Greece.","DOI":"10.1109\/ITSC45102.2020.9294422"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Raju, V.M., Gupta, V., and Lomate, S. (2019, January 29\u201331). Performance of Open Autonomous Vehicle Platforms: Autoware and Apollo. Proceedings of the 2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019, Bombay, India.","DOI":"10.1109\/I2CT45611.2019.9033734"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1007\/978-3-319-67361-5_40","article-title":"AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles","volume":"Volume 5","author":"Shah","year":"2018","journal-title":"Field and Service Robotics"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Cao, L., Feng, X., Liu, J., and Zhou, G. (2024). Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan. Appl. Sci., 14.","DOI":"10.3390\/app14041354"},{"key":"ref_84","unstructured":"Kemeny, A., and M\u00e9rienne, F. (2010). Trends in Driving Simulation Design and Experiments. Driving Simulation Conference Europe 2010 Proceedings, Actes."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Winner, H., Lemmer, K., Form, T., and Mazzega, J. (2019). PEGASUS\u2014First Steps for the Safe Introduction of Automated Driving. Road Vehicle Automation 5, Springer.","DOI":"10.1007\/978-3-319-94896-6_16"},{"key":"ref_86","unstructured":"(2025, July 13). Safety Pool\u2014Powered by Deepen AI and WMG University of Warwick. Available online: https:\/\/www.safetypool.ai\/."},{"key":"ref_87","first-page":"3461","article-title":"MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning","volume":"45","author":"Li","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Kaljavesi, G., Kerbl, T., Betz, T., Mitkovskii, K., and Diermeyer, F. (2024, January 2\u20135). CARLA-Autoware-Bridge: Facilitating Autonomous Driving Research with a Unified Framework for Simulation and Module Development. Proceedings of the IEEE Intelligent Vehicles Symposium, Proceedings, Jeju, Republic of Korea.","DOI":"10.1109\/IV55156.2024.10588623"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TIV.2020.3003524","article-title":"Methods and Models for Simulating Autonomous Vehicle Sensors","volume":"5","author":"Elmquist","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Lethander, K., and Taylor, C. (2021, January 20\u201324). Conservative estimation of inertial sensor errors using allan variance data. Proceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021, St. Louis, MO, USA.","DOI":"10.33012\/2021.17921"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Fang, X., Song, D., Shi, C., Fan, L., and Hu, Z. (2022). Multipath Error Modeling Methodology for GNSS Integrity Monitoring Using a Global Optimization Strategy. Remote Sens., 14.","DOI":"10.3390\/rs14092130"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Kim, J., Park, B.J., and Kim, J. (2023). Empirical Analysis of Autonomous Vehicle\u2019s LiDAR Detection Performance Degradation for Actual Road Driving in Rain and Fog. Sensors, 23.","DOI":"10.3390\/s23062972"},{"key":"ref_93","unstructured":"(2025, August 13). Cannot Get the Traffic_Light_Rois and the Image Raw in rviz2 When Executing Autoware+AWSIM Simulation Issue #5567 Autowarefoundation\/Autoware_Universe. Available online: https:\/\/github.com\/autowarefoundation\/autoware_universe\/issues\/5567?utm_source=chatgpt.com%3Futm_source%3Dchatgpt.com."},{"key":"ref_94","unstructured":"(2025, August 13). Integrate Trafficlight Detection as an Exemplary Case for Town10. Issue #8 TUMFTM\/Carla-Autoware-Bridge. Available online: https:\/\/github.com\/TUMFTM\/Carla-Autoware-Bridge\/issues\/8?utm_source=chatgpt.com."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1109\/TRO.2024.3521856","article-title":"Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior","volume":"41","author":"Burnett","year":"2024","journal-title":"IEEE Trans. Robot."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"16462","DOI":"10.1109\/TITS.2024.3409907","article-title":"Understanding LiDAR Performance for Autonomous Vehicles Under Snowfall Conditions","volume":"25","author":"Sun","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_97","unstructured":"Habib, A.F., Al-Durgham, M., Kersting, A.P., and Quackenbush, P. (2008). Error Budget of Lidar Systems and Quality Control of the Derived Point Cloud. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, International Society of Photogrammetry and Remote Sensing."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Meng, X., Wang, H., and Liu, B. (2017). A Robust Vehicle Localization Approach Based on GNSS\/IMU\/DMI\/LiDAR Sensor Fusion for Autonomous Vehicles. Sensors, 17.","DOI":"10.3390\/s17092140"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/BF02106512","article-title":"The Global Positioning System: Signals, measurements, and performance","volume":"1","author":"Enge","year":"1994","journal-title":"Int. J. Wirel. Inf. Netw."},{"key":"ref_100","unstructured":"Bosch Sensortec GmbH (2025, July 14). BMI160: Small, Low-Power Inertial Measurement Unit\u2014Data Sheet; Document No. BST-BMI160-DS000-09, Revision 1.0; published November 25, 2020. Available online: https:\/\/www.bosch-sensortec.com\/media\/boschsensortec\/downloads\/datasheets\/bst-bmi160-ds000.pdf."},{"key":"ref_101","unstructured":"InvenSense Inc. (2025, July 14). MPU-6000 and MPU-6050 Product Specification, Document No. PS-MPU-6000A-00, Revision 3.4, released 19 August 2013. Available online: https:\/\/invensense.tdk.com\/wp-content\/uploads\/2015\/02\/MPU-6000-Datasheet1.pdf."},{"key":"ref_102","unstructured":"Analog Devices, Inc. (2025, July 14). ADXL345: Digital Accelerometer Data Sheet, Rev. G, published 26 October 2015. Available online: https:\/\/www.analog.com\/media\/en\/technical-documentation\/data-sheets\/adxl345.pdf."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1109\/TBME.2006.875664","article-title":"Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing","volume":"53","author":"Sabatini","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"4614","DOI":"10.1109\/LRA.2022.3151970","article-title":"Camera-IMU Extrinsic Calibration Quality Monitoring for Autonomous Ground Vehicles","volume":"7","author":"Xiao","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"9919","DOI":"10.1109\/JSEN.2021.3059310","article-title":"Realistic LiDAR with Noise Model for Real-Time Testing of Automated Vehicles in a Virtual Environment","volume":"21","author":"Espineira","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_106","unstructured":"(2025, June 16). IEEE Xplore Full-Text PDF. Available online: https:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?arnumber=9925708."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Wang, Y., Hwang, J.N., Wang, G., Liu, H., Kim, K.J., Hsu, H.M., Cai, J., Zhang, H., Jiang, Z., and Gu, R. (2021, January 21\u201324). ROD2021 challenge: A summary for radar object detection challenge for autonomous driving applications. Proceedings of the ICMR 2021\u2014Proceedings of the 2021 International Conference on Multimedia Retrieval, Taipei, Taiwan.","DOI":"10.1145\/3460426.3463658"},{"key":"ref_108","unstructured":"Carballo, A., Lambert, J., Monrroy, A., Wong, D., Narksri, P., Kitsukawa, Y., Takeuchi, E., Kato, S., and Takeda, K. (November, January 19). LIBRE: The Multiple 3D LiDAR Dataset. Proceedings of the IEEE Intelligent Vehicles Symposium, Proceedings, Las Vegas, NV, USA."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/3\/94\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:45:36Z","timestamp":1760035536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/3\/94"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"references-count":108,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["informatics12030094"],"URL":"https:\/\/doi.org\/10.3390\/informatics12030094","relation":{},"ISSN":["2227-9709"],"issn-type":[{"type":"electronic","value":"2227-9709"}],"subject":[],"published":{"date-parts":[[2025,9,15]]}}}