187 citations as recorded by crossref.

1. A Study on the Effects of Multi-Vehicle Communication Failures and Their Data Compensation in Platooning Operation 睿. 蒙 https://doi.org/10.12677/CSA.2023.1312226
2. Snow coverage estimation using camera data for automated driving applications N. Goberville et al. https://doi.org/10.1016/j.trip.2023.100766
3. Toward standardized testing of automotive lidars: year two results T. Aspiras et al. https://doi.org/10.1117/1.OE.63.8.085102
4. Investigation of a DSRC‐based end of queue collision warning system by considering real freeway data Y. Liu et al. https://doi.org/10.1049/iet-its.2018.5045
5. Dynamic Object-Aware LiDAR Odometry Aided by Joint Weightings Estimation in Urban Areas F. Huang et al. https://doi.org/10.1109/TIV.2023.3338141
6. Understanding LiDAR Performance for Autonomous Vehicles Under Snowfall Conditions C. Sun et al. https://doi.org/10.1109/TITS.2024.3409907
7. On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera M. Kim et al. https://doi.org/10.3390/s21206733
8. Long range 3D imaging through atmospheric obscurants using array-based single-photon LiDAR P. Jiang et al. https://doi.org/10.1364/OE.487560
9. Range detection assessment of photonic radar under adverse weather perceptions V. Sharma & S. Sergeyev https://doi.org/10.1016/j.optcom.2020.125891
10. Long-Term Investigations of Weather Influence on Direct Time-of-Flight Lidar at 905nm M. Wichmann et al. https://doi.org/10.1109/JSEN.2021.3133658
11. High Speed Surface Illuminated Si Photodiode Using Microstructured Holes for Absorption Enhancements at 900–1000 nm Wavelength Y. Gao et al. https://doi.org/10.1021/acsphotonics.7b00486
12. Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning H. Chen et al. https://doi.org/10.1155/2021/5524356
13. Modeling and Simulation of Self-Stabilizing Control Traffic Flow Based on Data Compensation 亚. 李 https://doi.org/10.12677/CSA.2022.1211261
14. Radar Translation Network Between Sunny and Rainy Domains by Combination of KP-Convolution and CycleGAN J. Lee et al. https://doi.org/10.1109/OJITS.2023.3331437
15. Concept Study of a Self-localization System for Snow-covered Roads Using a Four-layer Laser Scanner T. Mimuro et al. https://doi.org/10.1007/s42154-019-00061-5
16. A Systematic Review of Influences and Test Methodologies: Evaluating the Impact of Adverse Weather Conditions on Perception Systems for Intelligent Vehicles B. Zhu et al. https://doi.org/10.1109/JIOT.2026.3705957
17. SOTIF-Oriented Perception Evaluation Method for Forward Obstacle Detection of Autonomous Vehicles J. Chu et al. https://doi.org/10.1109/JSYST.2023.3234200
18. Detection of passageways in natural foliage using biomimetic sonar R. Wang et al. https://doi.org/10.1088/1748-3190/ac7aff
19. A Multivariable Model for Predicting Automotive LiDAR Visibility Under Driving-In-Rain Conditions W. Pao et al. https://doi.org/10.3390/app16041835
20. A New Paradigm in High-Speed and High-Efficiency Silicon Photodiodes for Communication—Part I: Enhancing Photon–Material Interactions via Low-Dimensional Structures H. Cansizoglu et al. https://doi.org/10.1109/TED.2017.2779145
21. FogFusion: Robust 3D object detection based on camera-LiDAR fusion for autonomous driving in foggy weather conditions B. Zhang et al. https://doi.org/10.1177/09544070251327229
22. Layered SOTIF Analysis and 3σ-Criterion-Based Adaptive EKF for Lidar-Based Multi-Sensor Fusion Localization System on Foggy Days L. Cao et al. https://doi.org/10.3390/rs15123047
23. Adaptive strategy for CPPM single-photon collision avoidance LIDAR against dynamic crosstalk F. Zhang et al. https://doi.org/10.1364/OE.25.012237
24. Event-triggered autonomous platoon control against probabilistic sensor and actuator failures W. Yue & L. Wang https://doi.org/10.1080/00051144.2017.1323714
25. Analysis of Automotive Lidar Sensor Model Considering Scattering Effects in Regional Rain Environments M. Byeon & S. Yoon https://doi.org/10.1109/ACCESS.2020.2996366
26. A Framework to Analyze Noise Factors of Automotive Perception Sensors P. Chan et al. https://doi.org/10.1109/LSENS.2020.2996428
27. Evaluation and Compensation of the Effect of Dirt on Time-of-Flight 3D Imaging Systems Z. Liu & M. Conde https://doi.org/10.1109/JSEN.2022.3189773
28. In-Field Measurement and Methodology for Modeling and Validation of Precipitation Effects on Solid-State LiDAR Sensors M. Kettelgerdes & G. Elger https://doi.org/10.1109/JRFID.2023.3234999
29. 3D ToF LiDAR for Mobile Robotics in Harsh Environments: A Review T. Yang et al. https://doi.org/10.1142/S230138502530001X
30. Fog Removal Study on Fog Point Distribution and Multi-Scale Dynamic Thresholding in the Field of Point Cloud Defogging Z. Liu et al. https://doi.org/10.1142/S0218126626500751
31. Analysis of Factors Affecting the Severity of Automated Vehicle Crashes Using XGBoost Model Combining POI Data H. Chen et al. https://doi.org/10.1155/2020/8881545
32. Spoofing Attacks on FMCW Radars with Low-Cost Backscatter Tags A. Lazaro et al. https://doi.org/10.3390/s22062145
33. Problems related to the operation of autonomous vehicles in adverse weather conditions M. Brzozowski & K. Parczewski https://doi.org/10.19206/CE-168805
34. Full Waveform LiDAR for Adverse Weather Conditions A. Wallace et al. https://doi.org/10.1109/TVT.2020.2989148
35. Parallel LiDARs Meet the Foggy Weather Y. Liu et al. https://doi.org/10.1109/JRFID.2022.3203733
36. Experimental Analysis of Various Blockage Performance for LiDAR Sensor Cleaning Evaluation S. Son et al. https://doi.org/10.3390/s23052752
37. Assessing the Safety of Environment Perception in Automated Driving Vehicles M. Berk et al. https://doi.org/10.4271/09-08-01-0004
38. Spatially-Aware Reliability Modeling for BEV LiDAR 3D Vehicle Detection N. Hu et al. https://doi.org/10.3390/s26134229
39. LIDAR Point Cloud Augmentation for Dusty Weather Based on a Physical Simulation H. Lian et al. https://doi.org/10.3390/math12010141
40. Synthetic Point Cloud Generation for Autonomous Driving: A Survey and Taxonomy A. Iglesias et al. https://doi.org/10.1109/MITS.2026.3653178
41. Detection of a target hidden in dense cloud clutter using an echo pattern of pulsed laser radar systems Z. Bahmeh & H. Zangeneh https://doi.org/10.1088/1555-6611/acdbc2
42. Perceived rain dynamics on hydrophilic/hydrophobic lens surfaces and their influences on vehicle camera performance W. Pao et al. https://doi.org/10.1139/tcsme-2023-0156
43. Deep-learning blurring correction of images obtained from NIR single-pixel imaging C. Osorio Quero et al. https://doi.org/10.1364/JOSAA.488549
44. Adaptive Sensor Fusion for Robust Perception in Dense Fog: A Gated Vision and LiDAR Integration Framework F. Zhang et al. https://doi.org/10.3390/s26123728
45. Reliability Assessment of Safety-Critical Sensor Information: Does One Need a Reference Truth? M. Berk et al. https://doi.org/10.1109/TR.2019.2923735
46. A review of vehicle active safety control methods: From antilock brakes to semiautonomy A. Ahangarnejad et al. https://doi.org/10.1177/1077546320948656
47. Automotive System for Remote Surface Classification A. Bystrov et al. https://doi.org/10.3390/s17040745
48. Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots V. De Silva et al. https://doi.org/10.3390/s18082730
49. A Detection Method for Open–Close States of High-Voltage Disconnector in Smoky Environments L. Wang et al. https://doi.org/10.3390/s25051280
50. From clear to adverse: a survey of all-weather LiDAR semantic segmentation for autonomous driving Y. Ding et al. https://doi.org/10.1016/j.ait.2026.100061
51. 基于光学模型的雾天点云渲染方法 于. Yu Huichun et al. https://doi.org/10.3788/LOP241136
52. Research on Pulse Linear Array LiDAR Point Cloud Enhancement Algorithm in Snowy Environment X. Shi et al. https://doi.org/10.1088/1742-6596/2891/15/152005
53. A Stochastic Physical Simulation Framework to Quantify the Effect of Rainfall on Automotive Lidar M. Berk et al. https://doi.org/10.4271/2019-01-0134
54. Weather Classification Using an Automotive LIDAR Sensor Based on Detections on Asphalt and Atmosphere J. Vargas Rivero et al. https://doi.org/10.3390/s20154306
55. Investigation and Optimization of a Pulsed Laser Radar Transmitter for Detection Performance in a Cloud Turbulent Medium Z. Bahmeh & H. Zangeneh https://doi.org/10.1007/s40995-024-01642-8
56. A new search scheme using multi‐bee‐colony elite learning method for unmanned aerial vehicles in unknown environments W. Yue et al. https://doi.org/10.1002/oca.2918
57. Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions G. Abro et al. https://doi.org/10.62762/TSCC.2024.989358
58. A Survey of Validation and Verification Methods for AI-Based Vehicle Functions T. Aslandere & U. Durak https://doi.org/10.4271/12-08-04-0031
59. Analytic and numeric study of polarized light propagation in turbid media for lidar applications M. Petzi et al. https://doi.org/10.1364/AO.568350
60. DSRC-based rear-end collision warning system – An error-component safety distance model and field test X. Zhao et al. https://doi.org/10.1016/j.trc.2019.08.002
61. LiDAR Point Cloud Augmentation for Adverse Conditions Using Conditional Generative Model Y. Zhang et al. https://doi.org/10.3390/rs16122247
62. Characterization of Cubic Zirconia as a Lens Material Suitable for Autonomous Driving J. Lee et al. https://doi.org/10.3390/photonics9100701
63. From clear to snow: realistic LiDAR simulation via physical-statistical modeling C. Sun et al. https://doi.org/10.1016/j.eswa.2026.132768
64. LiDAR Adverse-Weather Simulation with Ground Effect for Robust 3D Object Detection X. Ju et al. https://doi.org/10.3390/app16094409
65. An Overview of Lidar Imaging Systems for Autonomous Vehicles S. Royo & M. Ballesta-Garcia https://doi.org/10.3390/app9194093
66. Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation P. Kolar et al. https://doi.org/10.3390/s20082180
67. Reliability analysis of IoV-based vehicle monitoring systems subject to cascading probabilistic common cause failures C. Wang et al. https://doi.org/10.1016/j.ress.2024.110605
68. Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions M. Jokela et al. https://doi.org/10.3390/app9112341
69. A Depth-Buffer-Based Lidar Model With Surface Normal Estimation M. Kirchengast & D. Watzenig https://doi.org/10.1109/TITS.2024.3371531
70. Comprehensive Evaluation and Defense against Adversarial Fog Attacks on LiDAR-based Autonomous Driving Y. Tanaka et al. https://doi.org/10.2197/ipsjjip.34.748
71. A data efficiency study of synthetic fog for object detection using the Clear2Fog pipeline M. Mohamed & X. Huang https://doi.org/10.1016/j.neucom.2026.134798
72. Physical model of snow precipitation interaction with a 3D lidar scanner G. Roy et al. https://doi.org/10.1364/AO.393059
73. An Extremely Low Noise-Equivalent Power Photoreceiver Using High-Gain InGaAs/AlGaAsSb APDs B. Sheridan et al. https://doi.org/10.1109/JLT.2024.3447284
74. Sensing with Polarized LIDAR in Degraded Visibility Conditions Due to Fog and Low Clouds A. Ronen et al. https://doi.org/10.3390/s21072510
75. Impact of Multi-Scattered LiDAR Returns in Fog D. Hevisov et al. https://doi.org/10.3390/s24165121
76. Asymmetric Gaussian Echo Model for LiDAR Intensity Correction X. Ma et al. https://doi.org/10.3390/rs16244625
77. Spatial Semantic Fuzzing for LiDAR-Based Autonomous Driving Perception Systems A. Guo et al. https://doi.org/10.1109/TSE.2025.3627580
78. Function-Specific Uncertainty Communication in Automated Driving A. Kunze et al. https://doi.org/10.4018/IJMHCI.2019040105
79. The Impact of Adverse Weather Conditions on Autonomous Vehicles: How Rain, Snow, Fog, and Hail Affect the Performance of a Self-Driving Car S. Zang et al. https://doi.org/10.1109/MVT.2019.2892497
80. Stochastic Optimization of Adaptive Cruise Control S. Zhang et al. https://doi.org/10.1007/s42979-022-01489-z
81. Model‐guided extremum seeking–case studies Y. Tan et al. https://doi.org/10.1002/acs.3366
82. Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors C. Linnhoff et al. https://doi.org/10.3390/s22145266
83. Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement S. Li et al. https://doi.org/10.1109/TMM.2023.3318317
84. An Efficient Adaptive Noise Removal Filter on Range Images for LiDAR Point Clouds M. Le et al. https://doi.org/10.3390/electronics12092150
85. RadarVerses in Metaverses: A CPSI-Based Architecture for 6S Radar Systems in CPSS Y. Liu et al. https://doi.org/10.1109/TSMC.2022.3228590
86. Method and Applications of Lidar Modeling for Virtual Testing of Intelligent Vehicles J. Zhao et al. https://doi.org/10.1109/TITS.2020.2978438
87. Performance Modeling a Near-Infrared ToF LiDAR Under Fog: A Data-Driven Approach T. Yang et al. https://doi.org/10.1109/TITS.2021.3102138
88. Photonic-Radar Based Multiple-Target Tracking Under Complex Traffic-Environments V. Sharma & L. Kumar https://doi.org/10.1109/ACCESS.2020.3045055
89. A Comprehensive Review of Autonomous Driving Algorithms: Tackling Adverse Weather Conditions, Unpredictable Traffic Violations, Blind Spot Monitoring, and Emergency Maneuvers C. Xu & R. Sankar https://doi.org/10.3390/a17110526
90. Perception and sensing for autonomous vehicles under adverse weather conditions: A survey Y. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2022.12.021
91. Polarised full-waveform warning LIDAR with dust backscattering suppression J. Liu et al. https://doi.org/10.1016/j.optlaseng.2023.107658
92. Roadside LiDAR Sensor Configuration Assessment and Optimization Methods for Vehicle Detection and Tracking in Connected and Automated Vehicle Applications Y. Ge et al. https://doi.org/10.1177/03611981231172949
93. Research on Multiple Sensors Vehicle Detection With EMD-Based Denoising J. Li et al. https://doi.org/10.1109/JIOT.2018.2890541
94. Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors M. Kettelgerdes et al. https://doi.org/10.3390/rs16132407
95. Effect of principal environmental parameters on performance of LFMCW photonic radar during transmission S. Chen et al. https://doi.org/10.1088/1361-6501/aca9e9
96. Automatic Vehicle Detection With Roadside LiDAR Data Under Rainy and Snowy Conditions J. Wu et al. https://doi.org/10.1109/MITS.2019.2926362
97. SCOPE: Spatial Context-Aware Pointcloud Encoder for Denoising Under the Adverse Weather Conditions H. Kim https://doi.org/10.3390/app151810113
98. Performance and Challenges of 3D Object Detection Methods in Complex Scenes for Autonomous Driving K. Wang et al. https://doi.org/10.1109/TIV.2022.3213796
99. Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review P. Sun et al. https://doi.org/10.3390/s22239316
100. Car2Car Communication Using a Modulated Backscatter and Automotive FMCW Radar A. Lazaro et al. https://doi.org/10.3390/s21113656
101. Adaptive Tube-Based Nonlinear MPC for Economic Autonomous Cruise Control of Plug-In Hybrid Electric Vehicles B. Sakhdari & N. Azad https://doi.org/10.1109/TVT.2018.2872654
102. Impact of Adverse Weather on Road Safety: A Survey of Test Methods for Enhancing Safety of Automated Vehicles and Sensor Robustness in Challenging Environmental Conditions M. Graf et al. https://doi.org/10.1109/ACCESS.2025.3622501
103. Reconstruction of the Aerosol Scattering Coefficient Profile in the Atmosphere from Echo-Pulse of a Short-Range Lidar A. Lisenko et al. https://doi.org/10.1007/s11182-023-02880-w
104. Ship Detection in Maritime Scenes under Adverse Weather Conditions Q. Zhang et al. https://doi.org/10.3390/rs16091567
105. Measurement of target range and doppler shift by incorporating PDM-enabled FMCW-based photonic radar A. Sharma et al. https://doi.org/10.1016/j.ijleo.2022.169191
106. High Resolution-Based Coherent Photonic Radar Sensor for Multiple Target Detections S. Chaudhary et al. https://doi.org/10.3390/jsan11030049
107. A Survey on the autonomous exploration of confined subterranean spaces: Perspectives from real-word and industrial robotic deployments H. Azpúrua et al. https://doi.org/10.1016/j.robot.2022.104304
108. Vehicle Collision Prediction under Reduced Visibility Conditions K. Chen & P. Hsiung https://doi.org/10.3390/s18093026
109. Comparing real and simulated performance for an off‐road autonomous ground vehicle in obstacle avoidance D. Carruth et al. https://doi.org/10.1002/rob.22289
110. Vehicle-Mounted LiDAR Multiobject Detection and Tracking Under Weak Point Cloud W. Liang et al. https://doi.org/10.1109/JSEN.2025.3586443
111. Simultaneous measurements of velocity and distance via a dual-path FMCW lidar system F. Zhang et al. https://doi.org/10.1016/j.optcom.2020.126066
112. Precise intrinsic calibration and geometrical stability analysis of automotive solid-state LiDAR sensors over temperature and lifetime M. Kettelgerdes et al. https://doi.org/10.1016/j.optlaseng.2026.109761
113. Evaluating weather impact on vehicles: a systematic review of perceived precipitation dynamics and testing methodologies W. Pao et al. https://doi.org/10.1088/2631-8695/ad2033
114. GAN-Based LiDAR Translation between Sunny and Adverse Weather for Autonomous Driving and Driving Simulation J. Lee et al. https://doi.org/10.3390/s22145287
115. Realistic LiDAR With Noise Model for Real-Time Testing of Automated Vehicles in a Virtual Environment J. Espineira et al. https://doi.org/10.1109/JSEN.2021.3059310
116. An Automotive LiDAR Performance Test Method in Dynamic Driving Conditions J. Park et al. https://doi.org/10.3390/s23083892
117. Event-triggered control of vehicular platoon system with time-varying delay and sensor faults K. Bansal & P. Mukhija https://doi.org/10.1177/0954407020932676
118. Cooperative Multi-Point Vehicular Positioning Using Millimeter-Wave Surface Reflection Z. Zhang et al. https://doi.org/10.1109/TWC.2020.3040267
119. Quantifying Automotive Lidar System Uncertainty in Adverse Weather: Mathematical Models and Validation B. Alavi et al. https://doi.org/10.3390/app15158191
120. Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar T. Goelles et al. https://doi.org/10.3390/s20133662
121. 单目三维成像（特邀） 陈. Chen Yiyu et al. https://doi.org/10.3788/CJL260658
122. An Adaptive Group of Density Outlier Removal Filter: Snow Particle Removal from LiDAR Data M. Le et al. https://doi.org/10.3390/electronics11192993
123. High-Efficiency SiGe/Si Heterojunction Phototransistor With Photon- Trapping Nanoholes Operating at 600–1000-nm Wavelength Y. Sha et al. https://doi.org/10.1109/TED.2022.3162801
124. Challenges, Advances and Future Prospects of Vehicle Detection in Snow I. Reza et al. https://doi.org/10.5772/acrt.20250135
125. Particle polarization lidar for precipitation particle classification Y. Shibata https://doi.org/10.1364/AO.448354
126. LiDAR-Based NDT Matching Performance Evaluation for Positioning in Adverse Weather Conditions J. Chang et al. https://doi.org/10.1109/JSEN.2023.3312911
127. Analysis of LiDAR and Camera Data in Real-World Weather Conditions for Autonomous Vehicle Operations N. Goberville et al. https://doi.org/10.4271/2020-01-0093
128. Photonic Sensor for Multiple Targets Detection under Adverse Weather Conditions in Autonomous Vehicles A. Sharma et al. https://doi.org/10.3390/jsan11040060
129. Sampled-Data Cooperative Adaptive Cruise Control of Vehicles With Sensor Failures G. Guo & W. Yue https://doi.org/10.1109/TITS.2014.2316016
130. A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors A. Haider et al. https://doi.org/10.3390/s23156891
131. BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data S. Reitmann et al. https://doi.org/10.3390/s21062144
132. Exploiting Redundancy for Reliability Analysis of Sensor Perception in Automated Driving Vehicles M. Berk et al. https://doi.org/10.1109/TITS.2019.2948394
133. Technical concepts of automotive LiDAR sensors: a review H. Holzhüter et al. https://doi.org/10.1117/1.OE.62.3.031213
134. Neural Network-Driven Reliability Analysis in Safety Evaluation of LiDAR-Based Automated Vehicles: Considering Highway Vertical Alignments and Adverse Weather Conditions M. Cai et al. https://doi.org/10.3390/electronics13050881
135. Challenges, Advances and Future Prospects of Vehicle Detection in Snow I. Reza et al. https://doi.org/10.5772/acrt.20250135
136. Analysis of Automotive Lidar Corner Cases Under Adverse Weather Conditions B. Alavi et al. https://doi.org/10.3390/electronics14234695
137. A Lightweight Range-Image Network for LiDAR Point-Cloud Denoising in Adverse Weather T. la et al. https://doi.org/10.1109/ACCESS.2026.3659577
138. SS-WDA: Unsupervised Domain Adaptative 3-D Detection From Clear to Fog via Noise Simulation and Self-Training Y. Huang et al. https://doi.org/10.1109/JSEN.2024.3486920
139. Explicit Geometry-Reflectance Domain Shift Modeling for Robust LiDAR Segmentation in Adverse Weather L. Yang et al. https://doi.org/10.1007/s11263-026-02959-0
140. Research on a DSRC-Based Rear-End Collision Warning Model X. Xiang et al. https://doi.org/10.1109/TITS.2013.2293771
141. Automation transparency: implications of uncertainty communication for human-automation interaction and interfaces A. Kunze et al. https://doi.org/10.1080/00140139.2018.1547842
142. Infrared Sensation-Based Salient Targets Enhancement Methods in Low-Visibility Scenes H. Tan et al. https://doi.org/10.3390/s22155835
143. Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data A. Narayanan et al. https://doi.org/10.1109/LRA.2020.2967738
144. Through-the-Wall Micro-Doppler De-Wiring Technique via Cycle-Consistent Adversarial Network S. Wang et al. https://doi.org/10.3390/electronics11010124
145. Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar Systems Y. Liu et al. https://doi.org/10.3390/s22249930
146. Challenges and Innovations in 3D Object Recognition: The Integration of LiDAR and Camera Sensors for Autonomous Applications G. Abro et al. https://doi.org/10.1016/j.trpro.2025.03.116
147. Architecture and Potential of Connected and Autonomous Vehicles M. Pipicelli et al. https://doi.org/10.3390/vehicles6010012
148. CMHT autonomous dataset: A multi-sensor dataset including radar and IR for autonomous driving H. Zhang et al. https://doi.org/10.1016/j.dib.2025.111552
149. Real-Time LiDAR Signal Augmentation for Testing and Evaluation of Autonomous Systems in Virtual Adverse Weather S. Hwang et al. https://doi.org/10.7467/KSAE.2024.32.9.703
150. Advancing low-carbon smart cities: Leveraging UAVs-enabled low-altitude economy principles and innovations H. Tan et al. https://doi.org/10.1016/j.rser.2025.115942
151. A General Approach for Simulating Rain Effects on Sensor Data in Real and Virtual Environments S. Hasirlioglu & A. Riener https://doi.org/10.1109/TIV.2019.2960944
152. Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR S. Gazdag et al. https://doi.org/10.1109/ACCESS.2025.3596857
153. Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds J. Park et al. https://doi.org/10.1109/ACCESS.2020.3020266
154. Reconstruction and Synthesis of Lidar Point Clouds of Spray Y. Shih et al. https://doi.org/10.1109/LRA.2022.3148443
155. Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization S. Shit & D. Ray https://doi.org/10.1117/1.JEI.32.5.050901
156. A Quantitative Analysis of Point Clouds from Automotive Lidars Exposed to Artificial Rain and Fog K. Montalban et al. https://doi.org/10.3390/atmos12060738
157. Life-threatening alcohol-related traffic crashes in adverse weather: a double-matched case–control analysis from Canada D. Redelmeier & F. Manzoor https://doi.org/10.1136/bmjopen-2018-024415
158. A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles X. Yu & M. Marinov https://doi.org/10.3390/su12083281
159. Bayesian inference of visibility in fog and smoke artificial conditions from 3D-LiDAR point clouds K. Montalban et al. https://doi.org/10.1016/j.jqsrt.2023.108666
160. What Happens for a ToF LiDAR in Fog? Y. Li et al. https://doi.org/10.1109/TITS.2020.2998077
161. Characterisation of the Tyre Spray Ejected Downstream of a Bluff Automotive Body C. Crickmore et al. https://doi.org/10.4271/2022-01-0893
162. Event-triggered platoon control of vehicles with time-varying delay and probabilistic faults Y. Wei et al. https://doi.org/10.1016/j.ymssp.2016.09.042
163. Performance Test of Autonomous Vehicle Lidar Sensors Under Different Weather Conditions L. Tang et al. https://doi.org/10.1177/0361198120901681
164. Real-time Contaminated Surface Determination Algorithm for Automotive LiDAR Sensors J. Won et al. https://doi.org/10.7467/KSAE.2023.31.7.531
165. CIDM: A comprehensive inpainting diffusion model for missing weather radar data with knowledge guidance W. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2025.02.001
166. A Review of Testing Object-Based Environment Perception for Safe Automated Driving M. Hoss et al. https://doi.org/10.1007/s42154-021-00172-y
167. Research on Improving Laser Ranging Accuracy in Adverse Weather Conditions Using Artificial Intelligence Noise Reduction Technology M. Zhang & J. Chen https://doi.org/10.1080/09500340.2026.2723477
168. Simulation of LiDAR Under Fog with Generative Adversarial Networks for Robust 3D Object Detection Y. Park et al. https://doi.org/10.1007/s12239-025-00319-4
169. Cooperative Adaptive Cruise Control of Vehicles Using a Resource-Efficient Communication Mechanism S. Wen et al. https://doi.org/10.1109/TIV.2018.2886676
170. Point cloud processing under adverse weather: a survey of datasets, enhancement, and denoising methods Z. Gu et al. https://doi.org/10.1007/s11760-025-04352-9
171. Predicting the Influence of Rain on LIDAR in ADAS C. Goodin et al. https://doi.org/10.3390/electronics8010089
172. Learn to Model and Filter Point Cloud Noise for a Near-Infrared ToF LiDAR in Adverse Weather T. Yang et al. https://doi.org/10.1109/JSEN.2023.3298909
173. Deep Learning-Based Real-Time Vehicle Tire and Tank Temperature Monitoring Using Thermal Cameras Y. Hu et al. https://doi.org/10.3390/app16062656
174. The Effect of Spray Water on an Automotive LIDAR Sensor: A Real-Time Simulation Study J. Vargas Rivero et al. https://doi.org/10.1109/TIV.2021.3067892
175. Swept-wavelength lasers based on GaSb gain-chip technology for non-invasive biomedical sensing applications in the 1.7–2.5 μm wavelength range A. Vizbaras et al. https://doi.org/10.1364/BOE.9.004834
176. Full Physics Simulation Study of Guardrail Radar-Returns for 77 GHz Automotive Radar Systems U. Chipengo https://doi.org/10.1109/ACCESS.2018.2881101
177. Hierarchical Attention-Based Sensor Fusion Strategy for Depth Estimation in Diverse Weather M. Xiong et al. https://doi.org/10.1142/S1793351X23500022
178. Rear-End Collision Avoidance-Based on Multi-Channel Detection Y. Xiang et al. https://doi.org/10.1109/TITS.2019.2930731
179. A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges M. Ahangar et al. https://doi.org/10.3390/s21030706
180. A Cost-Effective Photonic Radar Under Adverse Weather Conditions for Autonomous Vehicles by Incorporating a Frequency-Modulated Direct Detection Scheme A. Sharma et al. https://doi.org/10.3389/fphy.2021.747598
181. Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication R. Lazar et al. https://doi.org/10.3390/s23177576
182. Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities S. Muckenhuber et al. https://doi.org/10.3390/s20113309
183. A Hierarchical LiDAR Simulation Framework Incorporating Physical Attenuation Response in Autonomous Driving Scenarios T. Huang et al. https://doi.org/10.1109/TITS.2023.3340676
184. A practical CARLA-based dust simulation framework for LiDAR perception in open-pit mines Z. Zhao & L. Bi https://doi.org/10.1088/1361-6501/ae70ce
185. CNN-Based Lidar Point Cloud De-Noising in Adverse Weather R. Heinzler et al. https://doi.org/10.1109/LRA.2020.2972865
186. Virtual Testing of Automated Driving Systems. A Survey on Validation Methods R. Dona & B. Ciuffo https://doi.org/10.1109/ACCESS.2022.3153722
187. 3D vehicle detection improving method fusing image and LiDAR in foggy environment S. Niu et al. https://doi.org/10.26599/HTRD.2025.9480083