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    <pageNumber>8</pageNumber>
    <edition/>
    <issue>196</issue>
    <volume>2026</volume>
    <articleNumber>109396</articleNumber>
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    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
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    <completedDate>2025-12-08</completedDate>
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    <title language="eng">Evaluating optical performance degradation of automotive cameras under accelerated aging</title>
    <abstract language="eng">Automotive cameras are subject to environmental stress, which degrades performance by reducing image sharpness. To qualify for automotive use and to ensure that the cameras maintain sharpness according to the hard requirements of end-of-line testing, cameras have to undergo standardized accelerated aging tests. These tests are performed to demonstrate reliability and functional safety over lifetime. Few studies have been published that demonstrate how aging contributes to the degradation of optical performance. This study addresses this gap by combining accelerated thermal aging with sharpness tracking to investigate degradation over time. To quantify sharpness degradation, six series-production cameras were subjected to accelerated thermal aging between −40◦𝐶 and +85◦𝐶. Each camera underwent 2000 aging cycles, equivalent to 80% of their lifetime based on the Coffin-Manson model of the LV124 standard. Sharpness was measured by calculating the Spatial Frequency Response (SFR) from images captured of a double-cross reticle projected by a virtual object generator with three illumination wavelengths (625nm, 520nm, and 470nm). The change in sharpness was evaluated with SFR50 and SFR at 60 line pairs per millimeter (SFR@60). During the first 250 cycles, a wear-in effect was observed, where sharpness increased before leveling off, as seen previously. The results also indicated a slow decline in sharpness showing long-term stability. Analysis indicated that before aging, the best focal plane was located closer to the focal position of the red wavelength, which lies furthest from the objective. By the end of the aging process, the best focal plane had shifted toward the focal position of the blue wavelength, which is located closer to the objective. This suggests a forward movement of the image sensor due to aging. Even after 2000 cycles, all cameras maintained an SFR@60 above 0.5. A Random Forest regression model was trained to predict the age based on the SFR curves, achieving a mean absolute error of 126 cycles and a 𝑅2 score of 0.96.</abstract>
    <parentTitle language="eng">Optics and Lasers in Engineering</parentTitle>
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      <first_name>Amit</first_name>
      <last_name>Pandey</last_name>
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      <first_name>Stephan</first_name>
      <last_name>Kühn</last_name>
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    <author>
      <first_name>Alexander</first_name>
      <last_name>Weis</last_name>
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      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
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  <doc>
    <id>6345</id>
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    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>17</pageNumber>
    <edition/>
    <issue/>
    <volume>15</volume>
    <articleNumber>38489</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
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    <completedDate>2025-11-10</completedDate>
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    <title language="eng">Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection</title>
    <abstract language="eng">Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin).</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-63458</identifier>
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    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
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    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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  </doc>
  <doc>
    <id>5985</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
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    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <completedDate>2025-06-12</completedDate>
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    <title language="eng">Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models</title>
    <abstract language="eng">In the realm of automated driving simulation and sensor modeling, the need for highly accurate sensor models is paramount for ensuring the reliability and safety of advanced driving assistance systems (ADAS). Hence, numerous works focus on the development of high-fidelity models of ADAS sensors, such as camera, Radar as well as modern LiDAR systems to simulate the sensor behavior in different driving scenarios, even under varying environmental conditions, considering for example adverse weather effects. However, aging effects of sensors, leading to suboptimal system performance, are mostly overlooked by current simulation techniques. This paper introduces a cutting-edge Hardware-in-the-Loop (HiL) test bench designed for the automated, accelerated aging and characterization of Automotive LiDAR sensors. The primary objective of this research is to address the aging effects of LiDAR sensors over the product life cycle, specifically focusing on aspects such as laser beam profile deterioration, output power reduction and intrinsic parameter drift, which are mostly neglected in current sensor models. By that, this proceeding research is intended to path the way, not only towards identifying and modeling respective degradation effects, but also to suggest quantitative model validation metrics.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59856</identifier>
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    <author>
      <first_name>Marcel</first_name>
      <last_name>Kettelgerdes</last_name>
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    <author>
      <first_name>Tjorven</first_name>
      <last_name>Hillmann</last_name>
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    <author>
      <first_name>Thomas</first_name>
      <last_name>Hirmer</last_name>
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    <author>
      <first_name>Hüseyin</first_name>
      <last_name>Erdogan</last_name>
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    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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  <doc>
    <id>5942</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>26</pageNumber>
    <edition/>
    <issue>11</issue>
    <volume>25</volume>
    <articleNumber>3422</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
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    <completedDate>2025-06-02</completedDate>
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    <title language="eng">Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection</title>
    <abstract language="eng">Mono RGB cameras and automotive radar sensors provide a complementary information set that makes them excellent candidates for sensor data fusion to obtain robust traffic user detection. This has been widely used in the vehicle domain and recently introduced in roadside-mounted smart infrastructure-based road user detection. However, the performance of the most commonly used late fusion methods often degrades when the camera fails to detect road users in adverse environmental conditions. The solution is to fuse the data using deep neural networks at the early stage of the fusion pipeline to use the complete data provided by both sensors. Research has been carried out in this area, but is limited to vehicle-based sensor setups. Hence, this work proposes a novel deep neural network to jointly fuse RGB mono-camera images and 3D automotive radar point cloud data to obtain enhanced traffic user detection for the roadside-mounted smart infrastructure setup. Projected radar points are first used to generate anchors in image regions with a high likelihood of road users, including areas not visible to the camera. These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. Valid detections are then used to segment radar points by instance, and the results are post-processed to produce final road user detections in the ground plane. The trained model is evaluated for different light and weather conditions using ground truth data from a lidar sensor. It provides a precision of 92%, recall of 78%, and F1-score of 85%. The proposed deep fusion methodology has 33%, 6%, and 21% absolute improvement in precision, recall, and F1-score, respectively, compared to object-level spatial fusion output.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
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    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
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    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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    <id>5836</id>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
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    <publisherName>arXiv</publisherName>
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    <completedDate>2025-04-15</completedDate>
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    <title language="eng">Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors</title>
    <abstract language="eng">Intelligent Transportation Systems (ITS) can benefit from roadside 4D mmWave radar sensors for large-scale traffic monitoring due to their weatherproof functionality, long sensing range and low manufacturing cost. However, the localization method using external measurement devices has limitations in urban environments. Furthermore, if the sensor mount exhibits changes due to environmental influences, they cannot be corrected when the measurement is performed only during the installation. In this paper, we propose self-localization of roadside radar data using Extended Object Tracking (EOT). The method analyses both the tracked trajectories of the vehicles observed by the sensor and the aerial laser scan of city streets, assigns labels of driving behaviors such as "straight ahead", "left turn", "right turn" to trajectory sections and road segments, and performs Semantic Iterative Closest Points (SICP) algorithm to register the point cloud. The method exploits the result from a down stream task -- object tracking -- for localization. We demonstrate high accuracy in the sub-meter range along with very low orientation error. The method also shows good data efficiency. The evaluation is done in both simulation and real-world tests.</abstract>
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    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
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    <author>
      <first_name>Qiuyu</first_name>
      <last_name>Xu</last_name>
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    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
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      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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      <first_name>Jürgen</first_name>
      <last_name>Beyerer</last_name>
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    <collection role="persons" number="49574">Kefferpütz, Klaus</collection>
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    <completedDate>2025-01-29</completedDate>
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    <title language="eng">Automotive LiDAR Based Precipitation State Estimation Using Physics Informed Spatio-Temporal 3D Convolutional Neural Networks (PIST-CNN)</title>
    <parentTitle language="eng">2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)</parentTitle>
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    <title language="eng">Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors</title>
    <abstract language="eng">With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion.</abstract>
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    <title language="eng">First steps towards real-world traffic signal control optimisation by reinforcement learning</title>
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