From a2e34c711825b89e89a204ea88137fa570c09a13 Mon Sep 17 00:00:00 2001 From: Timo Hinsemann <86418012+TimoHinsemann@users.noreply.github.com> Date: Mon, 7 Aug 2023 08:52:53 +0200 Subject: [PATCH] Removing of object identification related nodes (#33) * Extra node for artificial light intensity (before it was combined with moonlight- and starlight-intensity) * Nodes removed --- data.json | 429 ++---------------------------------------------------- 1 file changed, 12 insertions(+), 417 deletions(-) diff --git a/data.json b/data.json index 61ef9f3..be4a002 100755 --- a/data.json +++ b/data.json @@ -1,20 +1,20 @@ [ { "id": "0", - "parentIds": ["76","78"], + "parentIds": [], "title": "False negative detection", "decomBlock": "Detection identification", "description": "Missing detection, compared to an ideally detected surrounding.", - "references": "[76, Zhang et al., Lidar-based Object Classification with Explicit Occlusion Modeling, http://arxiv.org/abs/1907.04057] [78, Espineira et al., Realistic LiDAR With Noise Model for Real-Time Testing of Automated Vehicles in a Virtual Environment, https://ieeexplore.ieee.org/document/9354172/]", + "references": "", "nodeType": "effect" }, { "id": "1", - "parentIds": ["41"], + "parentIds": [], "title": "False positive detection", "decomBlock": "Detection identification", "description": "Additional detection, compared to an ideally detected surrounding.", - "references": "[41, Wu et al., Automatic Vehicle Detection With Roadside LiDAR Data Under Rainy and Snowy Conditions, https://ieeexplore.ieee.org/document/9006865/, False positive detections referred as noise points here.]", + "references": "[]", "nodeType": "effect" }, { @@ -62,60 +62,6 @@ "references": "[56, Wu et al., Real-Time Queue Length Detection with Roadside LiDAR Data, https://www.mdpi.com/1424-8220/20/8/2342] [79, Wu et al., Real-Time Queue Length Detection with Roadside LiDAR Data, https://www.mdpi.com/1424-8220/20/8/2342]", "nodeType": "designParameter" }, - { - "id": "8", - "parentIds": ["44", "45"], - "title": "Vibration of Lidar while driving", - "decomBlock": "Emission", - "description": "Oscillation of Lidar sensor while driving the vehicle.", - "references": "[44, Periu et al., Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection., https://library.csbe-scgab.ca/publications/cbe-journal/browse/6480-isolation-of-vibrations-transmitted-to-a-lidar-sensor-mounted-on-an-agricultural-vehicle-to-improve-obstacle-detection] [45, Periu et al., Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection., https://library.csbe-scgab.ca/publications/cbe-journal/browse/6480-isolation-of-vibrations-transmitted-to-a-lidar-sensor-mounted-on-an-agricultural-vehicle-to-improve-obstacle-detection]", - "nodeType": "effect" - }, - { - "id": "9", - "parentIds": ["8"], - "title": "Translation and rotation of vehicle about its centre of gravity", - "decomBlock": "Emission", - "description": "Spatial movement with all degrees of freedom taken into consideration.", - "references": "[8, Periu et al., Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection., https://library.csbe-scgab.ca/publications/cbe-journal/browse/6480-isolation-of-vibrations-transmitted-to-a-lidar-sensor-mounted-on-an-agricultural-vehicle-to-improve-obstacle-detection]", - "nodeType": "effect" - }, - { - "id": "10", - "parentIds": ["9"], - "title": "Travel speed", - "decomBlock": "Emission", - "description": "Velocity of Lidar equipped vehicle.", - "references": "[9, Periu et al., Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection., https://library.csbe-scgab.ca/publications/cbe-journal/browse/6480-isolation-of-vibrations-transmitted-to-a-lidar-sensor-mounted-on-an-agricultural-vehicle-to-improve-obstacle-detection]", - "nodeType": "systemIndependent" - }, - { - "id": "11", - "parentIds": ["9"], - "title": "Type of terrain", - "decomBlock": "Emission", - "description": "Type of terrain where Lidar equipped vehicle is being driven determines state of ground unevenness.", - "references": "[9, Periu et al., Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection., https://library.csbe-scgab.ca/publications/cbe-journal/browse/6480-isolation-of-vibrations-transmitted-to-a-lidar-sensor-mounted-on-an-agricultural-vehicle-to-improve-obstacle-detection]", - "nodeType": "systemIndependent" - }, - { - "id": "12", - "parentIds": ["8"], - "title": "Missing support bars", - "decomBlock": "Emission", - "description": "No use of support bars to connect the Lidar system with a less vibrating vehicle part.", - "references": "[8, Periu et al., Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection., https://library.csbe-scgab.ca/publications/cbe-journal/browse/6480-isolation-of-vibrations-transmitted-to-a-lidar-sensor-mounted-on-an-agricultural-vehicle-to-improve-obstacle-detection]", - "nodeType": "designParameter" - }, - { - "id": "13", - "parentIds": ["8"], - "title": "Missing stabilization system", - "decomBlock": "Emission", - "description": "No use of a stabilization system consisting of various vibration isolators.", - "references": "[8, Periu et al., Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection., https://library.csbe-scgab.ca/publications/cbe-journal/browse/6480-isolation-of-vibrations-transmitted-to-a-lidar-sensor-mounted-on-an-agricultural-vehicle-to-improve-obstacle-detection]", - "nodeType": "designParameter" - }, { "id": "14", "parentIds": ["73"], @@ -134,211 +80,13 @@ "references": "[111, Brown and Arnold, Fundamentals of Laser-Material Interaction and Application to Multiscale Surface Modification, http://link.springer.com/10.1007/978-3-642-10523-4_4, p.93.] [110, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two.] [110, Wei et al., Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance, https://linkinghub.elsevier.com/retrieve/pii/S0924271612000378, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two.] [110, Gotzig and Geduld, Automotive LIDAR, http://link.springer.com/10.1007/978-3-319-12352-3_18, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two. See p.415.] [112, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two.] [112, Wei et al., Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance, https://linkinghub.elsevier.com/retrieve/pii/S0924271612000378, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two.] [112, Gotzig and Geduld, Automotive LIDAR, http://link.springer.com/10.1007/978-3-319-12352-3_18, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two. See p.415.]", "nodeType": "designParameter" }, - { - "id": "16", - "parentIds": ["137"], - "title": "Horizontal/vertical scan angle", - "decomBlock": "Emission", - "description": "Horizontal scan angle referred as azimuth angle, vertical scan angle referred as zenith angle. Gimballed alignment of emitter optics will lead to elliptic scan pattern.", - "references": "[137, Yoo et al., MEMS-based lidar for autonomous driving, http://link.springer.com/10.1007/s00502-018-0635-2] [137, Bailey and Mahaffee, Rapid measurement of the three-dimensional distribution of leaf orientation and the leaf angle probability density function using terrestrial LiDAR scanning, https://linkinghub.elsevier.com/retrieve/pii/S0034425717301037] [137, Höfle and Pfeifer, Correction of laser scanning intensity data: Data and model-driven approaches, https://linkinghub.elsevier.com/retrieve/pii/S0924271607000603]", - "nodeType": "designParameter" - }, { "id": "17", - "parentIds": ["137", "102"], + "parentIds": ["102"], "title": "Lidar/mirror spin rate/oscillation frequency", "decomBlock": "Emission", "description": "Freqeuency of oscillating/rotating components of emitter optics.", - "references": "[137, Yoo et al., MEMS-based lidar for autonomous driving, http://link.springer.com/10.1007/s00502-018-0635-2] [137, Benson et al., Lissajous-Like Scan Pattern for a Nodding Multi-Beam Lidar, https://asmedigitalcollection.asme.org/DSCC/proceedings-abstract/DSCC2018/51906/V002T24A007/270931] [102, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/]", - "nodeType": "designParameter" - }, - { - "id": "18", - "parentIds": ["137"], - "title": "Emission pulse repetition frequency", - "decomBlock": "Emission", - "description": "Number of pulses emitted per second.", - "references": "[137, Benson et al., Lissajous-Like Scan Pattern for a Nodding Multi-Beam Lidar, https://asmedigitalcollection.asme.org/DSCC/proceedings-abstract/DSCC2018/51906/V002T24A007/270931]", - "nodeType": "designParameter" - }, - { - "id": "19", - "parentIds": ["20"], - "title": "False negative object", - "decomBlock": "Object identification", - "description": "An object present in ground truth is not detected by the sensor.", - "references": "[20, Maehlisch et al., De-cluttering with Integrated Probabilistic Data Association for Multisensor Multitarget ACC Vehicle Tracking, http://ieeexplore.ieee.org/document/4290111/] [20, Jayaraman et al., LiDAR Based Sensor Verification, https://www.sae.org/content/2018-01-0043/]", - "nodeType": "effect" - }, - { - "id": "20", - "parentIds": [], - "title": "Object existence error", - "decomBlock": "Object identification", - "description": "Error in determination of the existence of an object.", - "references": "", - "nodeType": "effect" - }, - { - "id": "21", - "parentIds": [], - "title": "Object class error", - "decomBlock": "Object identification", - "description": "Error in determination of the object class.", - "references": "", - "nodeType": "effect" - }, - { - "id": "22", - "parentIds": ["20"], - "title": "False positive object", - "decomBlock": "Object identification", - "description": "An object not present in ground truth is detected by the sensor.", - "references": "[20, Maehlisch et al., De-cluttering with Integrated Probabilistic Data Association for Multisensor Multitarget ACC Vehicle Tracking, http://ieeexplore.ieee.org/document/4290111/] [20, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.416.]", - "nodeType": "effect" - }, - { - "id": "23", - "parentIds": [], - "title": "Object state error", - "decomBlock": "Object identification", - "description": "Error in determination of a state of an object.", - "references": "", - "nodeType": "effect" - }, - { - "id": "24", - "parentIds": ["31"], - "title": "Too few class characteristics specified from sensor data", - "decomBlock": "Object identification", - "description": "Number of deterministic class characteristics specified from sensor data is too low.", - "references": "[31, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.418.]", - "nodeType": "designParameter" - }, - { - "id": "25", - "parentIds": ["19", "22"], - "title": "Sensor- or application-dependent threshold", - "decomBlock": "Object identification", - "description": "Threshold set by sensor or application, e.g. quantized as the number of measurements that confirm the object or passed time since initialization of the object.", - "references": "[19, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.416.] [22, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.416.]", - "nodeType": "designParameter" - }, - { - "id": "26", - "parentIds": ["32", "28"], - "title": "Uncertainty in estimated static variables", - "decomBlock": "Object identification", - "description": "Error in static variables estimated by filter algorithm.", - "references": "[32, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413.] [32, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/] [28, Munz et al., Generic Centralized Multi Sensor Data Fusion Based on Probabilistic Sensor and Environment Models for Driver Assistance Systems, http://ieeexplore.ieee.org/document/5557917/] [28, Sualeh and Kim, Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking, https://www.mdpi.com/1424-8220/19/6/1474] [28, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.416-417.] [28, Abdul Rachman, 3D-LIDAR Multi Object Tracking for Autonomous Driving, https://www.semanticscholar.org/paper/3D-LIDAR-Multi-Object-Tracking-for-Autonomous-and-Rachman/bafc8fcdee9b22708491ea1293524ece9e314851, p.37-39.]", - "nodeType": "effect" - }, - { - "id": "27", - "parentIds": ["35", "33", "34", "36", "28"], - "title": "Uncertainty in estimated dynamic variables", - "decomBlock": "Object identification", - "description": "Error in dynamic variables estimated by filter algorithm.", - "references": "[35, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, Reference on object positioning error; here. Positioning error being an object pose error with respect to DIN EN ISO 8373. See reference on object positioning error on p.413.] [35, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/, Reference on object positioning error; here. Positioning error being an object pose error with respect to DIN EN ISO 8373.] [35, Periu et al., Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection., https://library.csbe-scgab.ca/publications/cbe-journal/browse/6480-isolation-of-vibrations-transmitted-to-a-lidar-sensor-mounted-on-an-agricultural-vehicle-to-improve-obstacle-detection, Reference on object positioning error; here. Positioning error being an object pose error with respect to DIN EN ISO 8373.] [35, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/, Reference on object orientation error; here. Orientation error being an object pose error with respect to DIN EN ISO 8373.] [35, Hama and Toda, Basic Experiment of LIDAR Sensor Measurement Directional Instability for Moving and Vibrating Object, https://iopscience.iop.org/article/10.1088/1757-899X/472/1/012017, Reference on object orientation error; here. Orientation error being an object pose error with respect to DIN EN ISO 8373.] [33, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413.] [33, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/] [34, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/] [36, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/] [28, Munz et al., Generic Centralized Multi Sensor Data Fusion Based on Probabilistic Sensor and Environment Models for Driver Assistance Systems, http://ieeexplore.ieee.org/document/5557917/] [28, Sualeh and Kim, Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking, https://www.mdpi.com/1424-8220/19/6/1474] [28, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.416-417.] [28, Abdul Rachman, 3D-LIDAR Multi Object Tracking for Autonomous Driving, https://www.semanticscholar.org/paper/3D-LIDAR-Multi-Object-Tracking-for-Autonomous-and-Rachman/bafc8fcdee9b22708491ea1293524ece9e314851, p.37-39.]", - "nodeType": "effect" - }, - { - "id": "28", - "parentIds": ["19", "22"], - "title": "Existence filtering error", - "decomBlock": "Object identification", - "description": "Bayes or bayes based filter, consisting of probability calculations.", - "references": "[19, Maehlisch et al., De-cluttering with Integrated Probabilistic Data Association for Multisensor Multitarget ACC Vehicle Tracking, http://ieeexplore.ieee.org/document/4290111/] [22, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.416-417.] [22, Maehlisch et al., De-cluttering with Integrated Probabilistic Data Association for Multisensor Multitarget ACC Vehicle Tracking, http://ieeexplore.ieee.org/document/4290111/]", - "nodeType": "effect" - }, - { - "id": "29", - "parentIds": ["31", "19", "22", "23"], - "title": "Object separation error", - "decomBlock": "Object identification", - "description": "Error in separation of object parts or multiple objects.", - "references": "[31, Zhang et al., Lidar-based Object Classification with Explicit Occlusion Modeling, http://arxiv.org/abs/1907.04057] [31, Chen et al., Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques, http://www.mdpi.com/2072-4292/10/7/1078] [31, Awrangjeb and Fraser, Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs, http://www.mdpi.com/2072-4292/6/5/3716] [19, Chen et al., Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques, http://www.mdpi.com/2072-4292/10/7/1078] [22, Chen et al., Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques, http://www.mdpi.com/2072-4292/10/7/1078] [23, Chen et al., Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques, http://www.mdpi.com/2072-4292/10/7/1078]", - "nodeType": "effect" - }, - { - "id": "30", - "parentIds": ["21"], - "title": "Error in classifier learning procedure", - "decomBlock": "Object identification", - "description": "Error in specified or self generated class characteristics by learing procedure.", - "references": "[21, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.418.]", - "nodeType": "designParameter" - }, - { - "id": "31", - "parentIds": ["21"], - "title": "Class filtering error", - "decomBlock": "Object identification", - "description": "Bayes or bayes based filter, consisting of probability calculations.", - "references": "[21, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.418.] [21, Zhang et al., Lidar-based Object Classification with Explicit Occlusion Modeling, http://arxiv.org/abs/1907.04057] [21, Chen et al., Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques, http://www.mdpi.com/2072-4292/10/7/1078] [21, Awrangjeb and Fraser, Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs, http://www.mdpi.com/2072-4292/6/5/3716]", - "nodeType": "effect" - }, - { - "id": "32", - "parentIds": ["23", "31"], - "title": "Object dimension error", - "decomBlock": "Object identification", - "description": "Error in length/width, height, depth of object cluster, commonly expressed by a rectangle model.", - "references": "[23, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413.] [23, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/] [31, Cui et al., Automatic Vehicle Tracking With Roadside LiDAR Data for the Connected-Vehicles System, https://ieeexplore.ieee.org/document/8721124/]", - "nodeType": "effect" - }, - { - "id": "33", - "parentIds": ["23"], - "title": "Object velocity error", - "decomBlock": "Object identification", - "description": "Error in velocity of object cluster, commonly expressed by a rectangle model.", - "references": "[23, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413.] [23, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/]", - "nodeType": "effect" - }, - { - "id": "34", - "parentIds": ["23"], - "title": "Object acceleration error", - "decomBlock": "Object identification", - "description": "Error in acceleration of object cluster, commonly expressed by a rectangle model.", - "references": "[23, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/]", - "nodeType": "effect" - }, - { - "id": "35", - "parentIds": ["22", "23", "31"], - "title": "Object pose error", - "decomBlock": "Object identification", - "description": "Error in determination of the pose of an object. Position and orientation are determining the pose of an object with respect to DIN EN ISO 8373. Thus, pose error sums up errors in estimated orientation angle of an object cluster, which is commonly expressed by a rectangle model, and in estimated position of an object cluster.", - "references": "[22, Sawade et al., V2X Attack Vectors and Risk Analysis for Automated Cooperative Driving, https://ieeexplore.ieee.org/document/9448795/, Positioning error being an object pose error with respect to DIN EN ISO 8373.] [23, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, Positioning error being an object pose error with respect to DIN EN ISO 8373. See reference on object state error on p.413.] [23, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/, Positioning error and orientation error both being an object pose error with respect to DIN EN ISO 8373.] [23, Tang et al., LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments, http://www.mdpi.com/1424-8220/15/7/16710, Positioning error being an object pose error with respect to DIN EN ISO 8373.] [31, Cui et al., Automatic Vehicle Tracking With Roadside LiDAR Data for the Connected-Vehicles System, https://ieeexplore.ieee.org/document/8721124/, Positioning error being an object pose error with respect to DIN EN ISO 8373.]", - "nodeType": "effect" - }, - { - "id": "36", - "parentIds": ["23"], - "title": "Object angular speed error", - "decomBlock": "Object identification", - "description": "Error in angular speed of orientation angle of object cluster, commonly expressed by a rectangle model.", - "references": "[23, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/]", - "nodeType": "effect" - }, - { - "id": "37", - "parentIds": ["19", "22"], - "title": "Existence filtering method", - "decomBlock": "Object identification", - "description": "Method, which is used to determine the existence of an object, possibly being integrated in tracking algorithm.", - "references": "[19, Munz et al., Generic Centralized Multi Sensor Data Fusion Based on Probabilistic Sensor and Environment Models for Driver Assistance Systems, http://ieeexplore.ieee.org/document/5557917/, Consideration of false-positive object probability and false-negative object probability depending on filtering method. Thus; leading to corresponding results in calculated existence probability.] [19, Sualeh and Kim, Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking, https://www.mdpi.com/1424-8220/19/6/1474, Consideration of false-positive object probability and false-negative object probability depending on filtering method. Thus; leading to corresponding results in calculated existence probability.] [19, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, Consideration of false-positive object probability and false-negative object probability depending on filtering method. Thus; leading to corresponding results in calculated existence probability. See p.416-417.] [19, Abdul Rachman, 3D-LIDAR Multi Object Tracking for Autonomous Driving, https://www.semanticscholar.org/paper/3D-LIDAR-Multi-Object-Tracking-for-Autonomous-and-Rachman/bafc8fcdee9b22708491ea1293524ece9e314851, Consideration of false-positive object probability and false-negative object probability depending on filtering method. Thus; leading to corresponding results in calculated existence probability. See p.21-39.] [22, Munz et al., Generic Centralized Multi Sensor Data Fusion Based on Probabilistic Sensor and Environment Models for Driver Assistance Systems, http://ieeexplore.ieee.org/document/5557917/, Consideration of false-positive object probability and false-negative object probability depending on filtering method. Thus; leading to corresponding results in calculated existence probability.] [22, Sualeh and Kim, Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking, https://www.mdpi.com/1424-8220/19/6/1474, Consideration of false-positive object probability and false-negative object probability depending on filtering method. Thus; leading to corresponding results in calculated existence probability.] [22, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, Consideration of false-positive object probability and false-negative object probability depending on filtering method. Thus; leading to corresponding results in calculated existence probability. See p.416-417.] [22, Abdul Rachman, 3D-LIDAR Multi Object Tracking for Autonomous Driving, https://www.semanticscholar.org/paper/3D-LIDAR-Multi-Object-Tracking-for-Autonomous-and-Rachman/bafc8fcdee9b22708491ea1293524ece9e314851, Consideration of false-positive object probability and false-negative object probability depending on filtering method. Thus; leading to corresponding results in calculated existence probability. See p.21-39.]", - "nodeType": "designParameter" - }, - { - "id": "38", - "parentIds": ["26", "27"], - "title": "State filtering method", - "decomBlock": "Object identification", - "description": "Method, which is used to determine the object states, possibly being integrated in tracking algorithm.", - "references": "[26, Munz et al., Generic Centralized Multi Sensor Data Fusion Based on Probabilistic Sensor and Environment Models for Driver Assistance Systems, http://ieeexplore.ieee.org/document/5557917/] [26, Sualeh and Kim, Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking, https://www.mdpi.com/1424-8220/19/6/1474] [26, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413-417.] [26, Abdul Rachman, 3D-LIDAR Multi Object Tracking for Autonomous Driving, https://www.semanticscholar.org/paper/3D-LIDAR-Multi-Object-Tracking-for-Autonomous-and-Rachman/bafc8fcdee9b22708491ea1293524ece9e314851, p.21-39.] [27, Munz et al., Generic Centralized Multi Sensor Data Fusion Based on Probabilistic Sensor and Environment Models for Driver Assistance Systems, http://ieeexplore.ieee.org/document/5557917/] [27, Sualeh and Kim, Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking, https://www.mdpi.com/1424-8220/19/6/1474] [27, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413-417.] [27, Abdul Rachman, 3D-LIDAR Multi Object Tracking for Autonomous Driving, https://www.semanticscholar.org/paper/3D-LIDAR-Multi-Object-Tracking-for-Autonomous-and-Rachman/bafc8fcdee9b22708491ea1293524ece9e314851, p.21-39.]", + "references": "[102, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/]", "nodeType": "designParameter" }, { @@ -350,15 +98,6 @@ "references": "[0, Espineira et al., Realistic LiDAR With Noise Model for Real-Time Testing of Automated Vehicles in a Virtual Environment, https://ieeexplore.ieee.org/document/9354172/]", "nodeType": "effect" }, - { - "id": "41", - "parentIds": ["29", "45", "44", "28"], - "title": "Clustering error", - "decomBlock": "Pre-processing", - "description": "Error in assignment of data points to point cloud of an object candidate.", - "references": "[29, Chen et al., Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques, http://www.mdpi.com/2072-4292/10/7/1078] [29, Awrangjeb and Fraser, Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs, http://www.mdpi.com/2072-4292/6/5/3716] [45, Castaño et al., Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models, http://www.mdpi.com/1424-8220/18/5/1508] [45, Reiser et al., Iterative individual plant clustering in maize with assembled 2D LiDAR data, https://linkinghub.elsevier.com/retrieve/pii/S0166361517304748] [44, Reiser et al., Iterative individual plant clustering in maize with assembled 2D LiDAR data, https://linkinghub.elsevier.com/retrieve/pii/S0166361517304748] [28, Munz et al., Generic Centralized Multi Sensor Data Fusion Based on Probabilistic Sensor and Environment Models for Driver Assistance Systems, http://ieeexplore.ieee.org/document/5557917/] [28, Sualeh and Kim, Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking, https://www.mdpi.com/1424-8220/19/6/1474] [28, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.416-417.] [28, Abdul Rachman, 3D-LIDAR Multi Object Tracking for Autonomous Driving, https://www.semanticscholar.org/paper/3D-LIDAR-Multi-Object-Tracking-for-Autonomous-and-Rachman/bafc8fcdee9b22708491ea1293524ece9e314851, p.37-39.]", - "nodeType": "effect" - }, { "id": "43", "parentIds": ["39", "1"], @@ -368,60 +107,6 @@ "references": "[39, Liu et al., A dual-rate multi-filter algorithm for LiDAR-aided indoor navigation systems, http://ieeexplore.ieee.org/document/6851467/] [39, Maksymova et al., Review of LiDAR Sensor Data Acquisition and Compression for Automotive Applications, http://www.mdpi.com/2504-3900/2/13/852, See signal to noise ratio (SNR)] [1, Lichti et al., Error Models and Propagation in Directly Georeferenced Terrestrial Laser Scanner Networks, http://ascelibrary.org/doi/10.1061/%28ASCE%290733-9453%282005%29131%3A4%28135%29, See blooming. Excess charges flowing into neighboring photodiodes or pixels; creating unintentionally measured power impulses and thus; creating false positive detections.] [1, Espineira et al., Realistic LiDAR With Noise Model for Real-Time Testing of Automated Vehicles in a Virtual Environment, https://ieeexplore.ieee.org/document/9354172/] [1, Shin et al., Illusion and Dazzle: Adversarial Optical Channel Exploits Against Lidars for Automotive Applications, http://link.springer.com/10.1007/978-3-319-66787-4_22, Raised noise floor due to saturation/blending security attack might reach detection threshold. See p.457]", "nodeType": "effect" }, - { - "id": "44", - "parentIds": ["26", "27"], - "title": "State filtering precision error", - "decomBlock": "Pre-processing", - "description": "Precision error after run through Bayes or bayes based filter, consisting of probability calculations.", - "references": "[26, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413-415.] [27, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413-415.]", - "nodeType": "effect" - }, - { - "id": "45", - "parentIds": ["26", "27"], - "title": "State filtering trueness error", - "decomBlock": "Pre-processing", - "description": "Trueness error after run through Bayes or bayes based filter, consisting of probability calculations. Mean approximated values do not converge with true values.", - "references": "[26, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413-416.] [27, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413-416.]", - "nodeType": "effect" - }, - { - "id": "46", - "parentIds": ["45", "44"], - "title": "Gridding error", - "decomBlock": "Pre-processing", - "description": "Error induced by gridding. Gridding being the construction of a grid map by assigning data points into grids of a certain size.", - "references": "[45, Smith et al., Investigating the Spatial Structure of Error in Digital Surface Models, https://www.isprs.org/PROCEEDINGS/XXXIV/3-W13/papers/Smith_ALSDD2003.pdf] [44, Smith et al., Investigating the Spatial Structure of Error in Digital Surface Models, https://www.isprs.org/PROCEEDINGS/XXXIV/3-W13/papers/Smith_ALSDD2003.pdf]", - "nodeType": "effect" - }, - { - "id": "47", - "parentIds": ["41"], - "title": "Clustering method", - "decomBlock": "Pre-processing", - "description": "Method, which is used to assign data points to point cloud of an object candidate. Common methods are hierarchy-based, centroid-based, distribution-based or density-based.", - "references": "[41, Sualeh and Kim, Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking, https://www.mdpi.com/1424-8220/19/6/1474] [41, Abdul Rachman, 3D-LIDAR Multi Object Tracking for Autonomous Driving, https://www.semanticscholar.org/paper/3D-LIDAR-Multi-Object-Tracking-for-Autonomous-and-Rachman/bafc8fcdee9b22708491ea1293524ece9e314851, p.17.]", - "nodeType": "designParameter" - }, - { - "id": "48", - "parentIds": ["46"], - "title": "Interpolation of raw data points", - "decomBlock": "Pre-processing", - "description": "Interpolation in this context being the transformation of the data points positions onto a plane of a certain size within a gridded map.", - "references": "[46, Smith et al., Investigating the Spatial Structure of Error in Digital Surface Models, https://www.isprs.org/PROCEEDINGS/XXXIV/3-W13/papers/Smith_ALSDD2003.pdf]", - "nodeType": "systemIndependent" - }, - { - "id": "49", - "parentIds": ["46"], - "title": "Grid size", - "decomBlock": "Pre-processing", - "description": "Grid size being the size of an even plane within a variety of connected even planes, which are creating a gridded map in their entirety.", - "references": "[46, Smith et al., Investigating the Spatial Structure of Error in Digital Surface Models, https://www.isprs.org/PROCEEDINGS/XXXIV/3-W13/papers/Smith_ALSDD2003.pdf]", - "nodeType": "designParameter" - }, { "id": "50", "parentIds": ["0"], @@ -458,15 +143,6 @@ "references": "[39, Zhou et al., Improvement of the signal to noise ratio of Lidar echo signal based on wavelet de-noising technique, https://linkinghub.elsevier.com/retrieve/pii/S0143816613000742] [39, Espineira et al., Realistic LiDAR With Noise Model for Real-Time Testing of Automated Vehicles in a Virtual Environment, https://ieeexplore.ieee.org/document/9354172/] [0, Kokhanenko et al., Expanding the dynamic range of a lidar receiver by the method of dynode-signal collection, https://www.osapublishing.org/abstract.cfm?URI=ao-41-24-5073] [0, Jiang et al., Invited Article: Optical dynamic range compression, http://aip.scitation.org/doi/10.1063/1.5051566]", "nodeType": "effect" }, - { - "id": "55", - "parentIds": ["31"], - "title": "Low number of points received from object", - "decomBlock": "Detection identification", - "description": "Low number of points belonging to an object received, impacting further classification.", - "references": "[31, Zhang et al., Lidar-based Object Classification with Explicit Occlusion Modeling, http://arxiv.org/abs/1907.04057] [31, Cui et al., Automatic Vehicle Tracking With Roadside LiDAR Data for the Connected-Vehicles System, https://ieeexplore.ieee.org/document/8721124/]", - "nodeType": "effect" - }, { "id": "56", "parentIds": ["0"], @@ -620,51 +296,6 @@ "references": "[54, Ansmann and Müller, Lidar and Atmospheric Aerosol Particles, http://link.springer.com/10.1007/0-387-25101-4_4, Lidar Equation: Geometric factor G(R): Overlap function O(R); p.109.] [54, Wandinger, Introduction to Lidar, https://link.springer.com/chapter/10.1007/0-387-25101-4_1, Lidar Equation: Geometric factor G(R): Overlap function O(R); p.8-9.]", "nodeType": "systemIndependent" }, - { - "id": "74", - "parentIds": ["44", "45"], - "title": "Standard deviated measuring error", - "decomBlock": "Signal propagation", - "description": "Possible measuring error, describable by stochastic disturbance variable modeled as zero-mean white noise.", - "references": "[44, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, Measurement equation z_k+1;k: Stochastic disturbance variable w_k+1; p.414-415.] [44, Cheng et al., Interactive Road Situation Analysis for Driver Assistance and Safety Warning Systems: Framework and Algorithms, http://ieeexplore.ieee.org/document/4114334/] [45, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, Measurement equation z_k+1;k: Stochastic disturbance variable w_k+1; p.414-415.] [45, Cheng et al., Interactive Road Situation Analysis for Driver Assistance and Safety Warning Systems: Framework and Algorithms, http://ieeexplore.ieee.org/document/4114334/]", - "nodeType": "systemIndependent" - }, - { - "id": "75", - "parentIds": ["28", "44", "45"], - "title": "Number of existence confirming measurements", - "decomBlock": "Signal propagation", - "description": "Number of measurements that confirm the existence of an object.", - "references": "[28, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.417.] [44, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413-415.] [45, Dietmayer, Predicting of Machine Perception for Automated Driving, http://link.springer.com/10.1007/978-3-662-48847-8_20, p.413-415.]", - "nodeType": "designParameter" - }, - { - "id": "76", - "parentIds": ["55", "41", "46"], - "title": "Low number density of points received from object", - "decomBlock": "Signal propagation", - "description": "Number density of points belonging to an object being too low with respect to further processing steps.", - "references": "[55, Wang et al., Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle, https://linkinghub.elsevier.com/retrieve/pii/S0921889015302633] [55, Cui et al., Automatic Vehicle Tracking With Roadside LiDAR Data for the Connected-Vehicles System, https://ieeexplore.ieee.org/document/8721124/] [41, Sualeh and Kim, Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking, https://www.mdpi.com/1424-8220/19/6/1474] [41, Wang et al., Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle, https://linkinghub.elsevier.com/retrieve/pii/S0921889015302633] [41, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/, More/less beams hitting an object in case of motion scan effect compared to static measurements. Number of additional or less beams hitting the object is then dependent on sensor resolution. Wrong cardinalities of clustered points in case of too few beams hitting the object is the result.] [46, Smith et al., Investigating the Spatial Structure of Error in Digital Surface Models, https://www.isprs.org/PROCEEDINGS/XXXIV/3-W13/papers/Smith_ALSDD2003.pdf]", - "nodeType": "effect" - }, - { - "id": "77", - "parentIds": ["76"], - "title": "Object too far from sensor", - "decomBlock": "Signal propagation", - "description": "Object being located too close or too far from sensor.", - "references": "[76, Sualeh and Kim, Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking, https://www.mdpi.com/1424-8220/19/6/1474] [76, Wang et al., Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle, https://linkinghub.elsevier.com/retrieve/pii/S0921889015302633] [76, Cui et al., Automatic Vehicle Tracking With Roadside LiDAR Data for the Connected-Vehicles System, https://ieeexplore.ieee.org/document/8721124/] [76, Kidono et al., Pedestrian recognition using high-definition LIDAR, http://ieeexplore.ieee.org/document/5940433/]", - "nodeType": "effect" - }, - { - "id": "78", - "parentIds": ["55"], - "title": "Incomplete point cloud", - "decomBlock": "Detection identification", - "description": "Point cloud belonging to an object being partially incomplete as points that should be existent with respect to the sensor resolution are missing.", - "references": "[55, Zhang et al., Lidar-based Object Classification with Explicit Occlusion Modeling, http://arxiv.org/abs/1907.04057]", - "nodeType": "effect" - }, { "id": "79", "parentIds": ["0"], @@ -674,22 +305,13 @@ "references": "[0, Zhang et al., Lidar-based Object Classification with Explicit Occlusion Modeling, http://arxiv.org/abs/1907.04057]", "nodeType": "systemIndependent" }, - { - "id": "80", - "parentIds": ["44", "45"], - "title": "Vibration of object", - "decomBlock": "Signal propagation", - "description": "High frequency oscillation of an object.", - "references": "[44, Hama and Toda, Basic Experiment of LIDAR Sensor Measurement Directional Instability for Moving and Vibrating Object, https://iopscience.iop.org/article/10.1088/1757-899X/472/1/012017] [45, Hama and Toda, Basic Experiment of LIDAR Sensor Measurement Directional Instability for Moving and Vibrating Object, https://iopscience.iop.org/article/10.1088/1757-899X/472/1/012017]", - "nodeType": "effect" - }, { "id": "81", - "parentIds": ["44", "45", "102"], + "parentIds": ["102"], "title": "Relative movement of object", "decomBlock": "Signal propagation", "description": "Object moving relative to LIDAR sensor.", - "references": "[44, Hama and Toda, Basic Experiment of LIDAR Sensor Measurement Directional Instability for Moving and Vibrating Object, https://iopscience.iop.org/article/10.1088/1757-899X/472/1/012017] [45, Hama and Toda, Basic Experiment of LIDAR Sensor Measurement Directional Instability for Moving and Vibrating Object, https://iopscience.iop.org/article/10.1088/1757-899X/472/1/012017] [102, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/]", + "references": "[102, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/]", "nodeType": "systemIndependent" }, { @@ -746,15 +368,6 @@ "references": "[54, Wandinger, Introduction to Lidar, https://link.springer.com/chapter/10.1007/0-387-25101-4_1, Lidar Equation: Transmission term T(R); p.10.] [54, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/, Laser-Radar-Equation: Transmittance of atmosphere T.]", "nodeType": "effect" }, - { - "id": "88", - "parentIds": ["80"], - "title": "Object trembling by wind", - "decomBlock": "Signal propagation", - "description": "Lightweighted object trembling by wind, e.g. leaves or banners.", - "references": "[80, Hama and Toda, Basic Experiment of LIDAR Sensor Measurement Directional Instability for Moving and Vibrating Object, https://iopscience.iop.org/article/10.1088/1757-899X/472/1/012017]", - "nodeType": "systemIndependent" - }, { "id": "89", "parentIds": ["43"], @@ -874,29 +487,20 @@ }, { "id": "102", - "parentIds": ["76", "45"], + "parentIds": [], "title": "Motion scan effect", "decomBlock": "Signal propagation", "description": "Vertical or horizontal scan of an object moving vertically or horizontally relative to the scanning direction is leading to a longer or shorter object scan period and, thus, to a directional expansion or compression of the resolution of the beams hitting the object and incorrect dimensions of the received object point cloud. The inequalities between detections without impact of motion scan effect and dynamic detections distorted by motion scan effect being referred as detection state errors.", - "references": "[76, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/, More/less beams hitting an object in case of motion scan effect compared to static measurements. Number of additional or less beams hitting the object is then dependent on sensor resolution.] [45, Gröll and Kapp, Effect of Fast Motion on Range Images Acquired by Lidar Scanners for Automotive Applications, http://ieeexplore.ieee.org/document/4203071/]", + "references": "", "nodeType": "effect" }, - { - "id": "103", - "parentIds": ["41"], - "title": "Short mutual distance of objects", - "decomBlock": "Signal propagation", - "description": "Short mutual distance of objects being the consequence of objects moving side by side or close positioned objects in general.", - "references": "[41, Aue et al., Efficient segmentation of 3D LIDAR point clouds handling partial occlusion, http://ieeexplore.ieee.org/document/5940442/] [41, Chen et al., Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques, http://www.mdpi.com/2072-4292/10/7/1078] [41, Awrangjeb and Fraser, Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs, http://www.mdpi.com/2072-4292/6/5/3716] [41, Dominguez et al., LIDAR based perception solution for autonomous vehicles, http://ieeexplore.ieee.org/document/6121753/]", - "nodeType": "systemIndependent" - }, { "id": "104", - "parentIds": ["45", "73", "77", "137", "117", "139", "54"], + "parentIds": ["73", "117", "139", "54"], "title": "Distance between sensor and object", "decomBlock": "Signal propagation", "description": "Spatial distance between Lidar sensor and object.", - "references": "[45, Ogawa et al., Pedestrian detection and tracking using in-vehicle lidar for automotive application, http://ieeexplore.ieee.org/document/5940555/] [73, Ansmann and Müller, Lidar and Atmospheric Aerosol Particles, http://link.springer.com/10.1007/0-387-25101-4_4, Lidar Equation: Geometric factor G(R): Overlap function O(R); p.109.] [73, Wandinger, Introduction to Lidar, https://link.springer.com/chapter/10.1007/0-387-25101-4_1, Lidar Equation: Geometric factor G(R): Overlap function O(R); p.8-9.] [77, Wang et al., Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle, https://linkinghub.elsevier.com/retrieve/pii/S0921889015302633] [77, Cui et al., Automatic Vehicle Tracking With Roadside LiDAR Data for the Connected-Vehicles System, https://ieeexplore.ieee.org/document/8721124/] [77, Kidono et al., Pedestrian recognition using high-definition LIDAR, http://ieeexplore.ieee.org/document/5940433/] [137, Benson et al., Lissajous-Like Scan Pattern for a Nodding Multi-Beam Lidar, https://asmedigitalcollection.asme.org/DSCC/proceedings-abstract/DSCC2018/51906/V002T24A007/270931] [137, Höfle and Pfeifer, Correction of laser scanning intensity data: Data and model-driven approaches, https://linkinghub.elsevier.com/retrieve/pii/S0924271607000603] [117, Rapp et al., Advances in Single-Photon Lidar for Autonomous Vehicles: Working Principles; Challenges; and Recent Advances, https://ieeexplore.ieee.org/document/9127841/] [139, Kashani et al., A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration, http://www.mdpi.com/1424-8220/15/11/28099, See bleeding. No differentiation between fraction of main beam and fraction of light from across main beam cross section illuminating object parts; here.] [54, Ansmann and Müller, Lidar and Atmospheric Aerosol Particles, http://link.springer.com/10.1007/0-387-25101-4_4, Lidar Equation: Geometric factor G(R): Distance R; p.109.] [54, Wandinger, Introduction to Lidar, https://link.springer.com/chapter/10.1007/0-387-25101-4_1, Lidar Equation: Geometric factor G(R): Distance R; p.8-9.]", + "references": "[73, Ansmann and Müller, Lidar and Atmospheric Aerosol Particles, http://link.springer.com/10.1007/0-387-25101-4_4, Lidar Equation: Geometric factor G(R): Overlap function O(R); p.109.] [73, Wandinger, Introduction to Lidar, https://link.springer.com/chapter/10.1007/0-387-25101-4_1, Lidar Equation: Geometric factor G(R): Overlap function O(R); p.8-9.] [117, Rapp et al., Advances in Single-Photon Lidar for Autonomous Vehicles: Working Principles; Challenges; and Recent Advances, https://ieeexplore.ieee.org/document/9127841/] [139, Kashani et al., A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration, http://www.mdpi.com/1424-8220/15/11/28099, See bleeding. No differentiation between fraction of main beam and fraction of light from across main beam cross section illuminating object parts; here.] [54, Ansmann and Müller, Lidar and Atmospheric Aerosol Particles, http://link.springer.com/10.1007/0-387-25101-4_4, Lidar Equation: Geometric factor G(R): Distance R; p.109.] [54, Wandinger, Introduction to Lidar, https://link.springer.com/chapter/10.1007/0-387-25101-4_1, Lidar Equation: Geometric factor G(R): Distance R; p.8-9.]", "nodeType": "systemIndependent" }, { @@ -1187,15 +791,6 @@ "references": "[111, Li and Liang, Remote measurement of surface roughness; surface reflectance; and body reflectance with LiDAR, https://www.osapublishing.org/abstract.cfm?URI=ao-54-30-8904] [111, Li et al., Bidirectional reflectance distribution function based surface modeling of non-Lambertian using intensity data of light detection and ranging, https://www.osapublishing.org/abstract.cfm?URI=josaa-31-9-2055] [110, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two.] [110, Wei et al., Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance, https://linkinghub.elsevier.com/retrieve/pii/S0924271612000378, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two.] [110, Gotzig and Geduld, Automotive LIDAR, http://link.springer.com/10.1007/978-3-319-12352-3_18, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two. See p.415.] [112, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two.] [112, Wei et al., Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance, https://linkinghub.elsevier.com/retrieve/pii/S0924271612000378, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two.] [112, Gotzig and Geduld, Automotive LIDAR, http://link.springer.com/10.1007/978-3-319-12352-3_18, Mutual influence of absorption; reflection and transmission: A + R + T = 1 with hemispherical absorptance A; hemispherical reflectance R; hemispherical transmittance T. Thus; causes for one of these three inevitably affect the other two. See p.415.]", "nodeType": "systemIndependent" }, - { - "id": "137", - "parentIds": ["76"], - "title": "Horizontal/vertical resolution", - "decomBlock": "Signal propagation", - "description": "Density of emitted laser beams for a given distance.", - "references": "[76, Kidono et al., Pedestrian recognition using high-definition LIDAR, http://ieeexplore.ieee.org/document/5940433/] [76, Rosenberger et al., Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models, https://ieeexplore.ieee.org/document/8500511/, More/less beams hitting an object in case of motion scan effect compared to static measurements. Number of additional or less beams hitting the object is then dependent on sensor resolution.]", - "nodeType": "designParameter" - }, { "id": "139", "parentIds": ["105"],