Änderungen
Auf 24. November 2023 um 13:36:43 UTC, Felix Fröhling:
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Keine Felder wurden aktualisiert. Bitte Metadaten-Änderung ansehen für Details
f | 1 | { | f | 1 | { |
2 | "author": "[{\"author\": \"Felix Fr\u00f6hling\", \"author_email\": | 2 | "author": "[{\"author\": \"Felix Fr\u00f6hling\", \"author_email\": | ||
3 | \"Felix.Froehling@carissma.eu\"}]", | 3 | \"Felix.Froehling@carissma.eu\"}]", | ||
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29 | "language": "en", | 29 | "language": "en", | ||
30 | "licence_agreement": [ | 30 | "licence_agreement": [ | ||
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33 | "license_id": "CC0-1.0", | 33 | "license_id": "CC0-1.0", | ||
34 | "license_title": "Creative Commons Zero (CC0 1.0)", | 34 | "license_title": "Creative Commons Zero (CC0 1.0)", | ||
35 | "license_url": "https://creativecommons.org/publicdomain/zero/1.0/", | 35 | "license_url": "https://creativecommons.org/publicdomain/zero/1.0/", | ||
36 | "maintainer": "[{\"Maintainer Email\": | 36 | "maintainer": "[{\"Maintainer Email\": | ||
37 | \"eduardo.sanchezmorales@thi.de\", \"maintainer\": \"Alberto Flores | 37 | \"eduardo.sanchezmorales@thi.de\", \"maintainer\": \"Alberto Flores | ||
38 | Fern\u00e1ndez\", \"phone\": \"\", \"role\": \"\"}]", | 38 | Fern\u00e1ndez\", \"phone\": \"\", \"role\": \"\"}]", | ||
39 | "maintainer_email": null, | 39 | "maintainer_email": null, | ||
40 | "metadata_created": "2023-11-24T13:34:20.152557", | 40 | "metadata_created": "2023-11-24T13:34:20.152557", | ||
n | 41 | "metadata_modified": "2023-11-24T13:36:42.851482", | n | 41 | "metadata_modified": "2023-11-24T13:36:43.354525", |
42 | "name": | 42 | "name": | ||
43 | tic-traffic-motion-labeling-for-multi-modal-vehicle-route-prediction", | 43 | tic-traffic-motion-labeling-for-multi-modal-vehicle-route-prediction", | ||
44 | "notes": "Abstract: The prediction of the motion of traffic | 44 | "notes": "Abstract: The prediction of the motion of traffic | ||
45 | participants is a crucial aspect for the research and\r\ndevelopment | 45 | participants is a crucial aspect for the research and\r\ndevelopment | ||
46 | of Automated Driving Systems (ADSs). Recent approaches are based on | 46 | of Automated Driving Systems (ADSs). Recent approaches are based on | ||
47 | multi-modal\r\nmotion prediction, which requires the assignment of a | 47 | multi-modal\r\nmotion prediction, which requires the assignment of a | ||
48 | probability score to each of the multiple\r\npredicted motion | 48 | probability score to each of the multiple\r\npredicted motion | ||
49 | hypotheses. However, there is a lack of ground truth for this | 49 | hypotheses. However, there is a lack of ground truth for this | ||
50 | probability score in\r\nthe existing datasets. This implies that | 50 | probability score in\r\nthe existing datasets. This implies that | ||
51 | current Machine Learning (ML) models evaluate the | 51 | current Machine Learning (ML) models evaluate the | ||
52 | multiple\r\npredictions by comparing them with the single real | 52 | multiple\r\npredictions by comparing them with the single real | ||
53 | trajectory labeled in the dataset. In this work, a\r\nnovel data-based | 53 | trajectory labeled in the dataset. In this work, a\r\nnovel data-based | ||
54 | method named Probabilistic Traffic Motion Labeling (PROMOTING) is | 54 | method named Probabilistic Traffic Motion Labeling (PROMOTING) is | ||
55 | introduced\r\nin order to (a) generate probable future routes and (b) | 55 | introduced\r\nin order to (a) generate probable future routes and (b) | ||
56 | estimate their probabilities. PROMOTING is\r\npresented with the focus | 56 | estimate their probabilities. PROMOTING is\r\npresented with the focus | ||
57 | on urban intersections. The generation of probable future routes is | 57 | on urban intersections. The generation of probable future routes is | ||
58 | (a) based\r\non a real traffic dataset and consists of two steps: | 58 | (a) based\r\non a real traffic dataset and consists of two steps: | ||
59 | first, a clustering of intersections with similar road\r\ntopology, | 59 | first, a clustering of intersections with similar road\r\ntopology, | ||
60 | and second, a clustering of similar routes that are driven in each | 60 | and second, a clustering of similar routes that are driven in each | ||
61 | cluster from the first step.\r\nThe estimation of the route | 61 | cluster from the first step.\r\nThe estimation of the route | ||
62 | probabilities is (b) based on a frequentist approach that considers | 62 | probabilities is (b) based on a frequentist approach that considers | ||
63 | how\r\ntraffic participants will move in the future given their motion | 63 | how\r\ntraffic participants will move in the future given their motion | ||
64 | history. PROMOTING is evaluated with\r\nthe publicly available Lyft | 64 | history. PROMOTING is evaluated with\r\nthe publicly available Lyft | ||
65 | database. The results show that PROMOTING is an appropriate | 65 | database. The results show that PROMOTING is an appropriate | ||
66 | approach\r\nto estimate the probabilities of the future motion of | 66 | approach\r\nto estimate the probabilities of the future motion of | ||
67 | traffic participants in urban intersections. In this\r\nregard, | 67 | traffic participants in urban intersections. In this\r\nregard, | ||
68 | PROMOTING can be used as a labeling approach for the generation of a | 68 | PROMOTING can be used as a labeling approach for the generation of a | ||
69 | labeled dataset that\r\nprovides a probability score for probable | 69 | labeled dataset that\r\nprovides a probability score for probable | ||
70 | future routes. Such a labeled dataset currently does not exist\r\nand | 70 | future routes. Such a labeled dataset currently does not exist\r\nand | ||
71 | would be highly valuable for ML approaches with the task of | 71 | would be highly valuable for ML approaches with the task of | ||
72 | multi-modal motion prediction.\r\nThe code is made open source.", | 72 | multi-modal motion prediction.\r\nThe code is made open source.", | ||
73 | "num_resources": 1, | 73 | "num_resources": 1, | ||
74 | "num_tags": 1, | 74 | "num_tags": 1, | ||
75 | "organization": { | 75 | "organization": { | ||
76 | "approval_status": "approved", | 76 | "approval_status": "approved", | ||
77 | "created": "2023-06-27T14:06:24.055358", | 77 | "created": "2023-06-27T14:06:24.055358", | ||
78 | "description": "", | 78 | "description": "", | ||
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80 | "image_url": "2023-06-27-150145.628103THI.jpg", | 80 | "image_url": "2023-06-27-150145.628103THI.jpg", | ||
81 | "is_organization": true, | 81 | "is_organization": true, | ||
82 | "name": "technische-hochschule-ingolstadt-thi", | 82 | "name": "technische-hochschule-ingolstadt-thi", | ||
83 | "state": "active", | 83 | "state": "active", | ||
84 | "title": "Technische Hochschule Ingolstadt (THI)", | 84 | "title": "Technische Hochschule Ingolstadt (THI)", | ||
85 | "type": "organization" | 85 | "type": "organization" | ||
86 | }, | 86 | }, | ||
87 | "owner_org": "518ef23a-8d7b-4829-9b42-7a182a73cc0f", | 87 | "owner_org": "518ef23a-8d7b-4829-9b42-7a182a73cc0f", | ||
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93 | "allowed_users": "", | 93 | "allowed_users": "", | ||
94 | "cache_last_updated": null, | 94 | "cache_last_updated": null, | ||
95 | "cache_url": null, | 95 | "cache_url": null, | ||
96 | "created": "2023-11-24T13:36:42.882447", | 96 | "created": "2023-11-24T13:36:42.882447", | ||
97 | "datastore_active": false, | 97 | "datastore_active": false, | ||
98 | "description": "Probabilistic Traffic Motion Labeling for | 98 | "description": "Probabilistic Traffic Motion Labeling for | ||
99 | Multi-Modal Vehicle Route Prediction", | 99 | Multi-Modal Vehicle Route Prediction", | ||
100 | "format": "pdf", | 100 | "format": "pdf", | ||
101 | "hash": "", | 101 | "hash": "", | ||
102 | "id": "f5782258-4422-4e3f-af74-9b65379cd20c", | 102 | "id": "f5782258-4422-4e3f-af74-9b65379cd20c", | ||
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105 | "mimetype": "application/pdf", | 105 | "mimetype": "application/pdf", | ||
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107 | "name": "Article", | 107 | "name": "Article", | ||
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111 | "restricted": "{\"level\": \"public\"}", | 111 | "restricted": "{\"level\": \"public\"}", | ||
112 | "size": 1336988, | 112 | "size": 1336988, | ||
113 | "state": "active", | 113 | "state": "active", | ||
114 | "url": | 114 | "url": | ||
115 | d20c/download/probabilistic_traffic_motion_labeling_for_multi-mo.pdf", | 115 | d20c/download/probabilistic_traffic_motion_labeling_for_multi-mo.pdf", | ||
116 | "url_type": "upload" | 116 | "url_type": "upload" | ||
117 | } | 117 | } | ||
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119 | "spatial": "", | 119 | "spatial": "", | ||
t | 120 | "state": "draft", | t | 120 | "state": "active", |
121 | "tags": [ | 121 | "tags": [ | ||
122 | { | 122 | { | ||
123 | "display_name": "Article", | 123 | "display_name": "Article", | ||
124 | "id": "e80a4ad9-1e6b-48d4-93f9-34e9d89bc67b", | 124 | "id": "e80a4ad9-1e6b-48d4-93f9-34e9d89bc67b", | ||
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127 | "vocabulary_id": null | 127 | "vocabulary_id": null | ||
128 | } | 128 | } | ||
129 | ], | 129 | ], | ||
130 | "title": "Probabilistic Traffic Motion Labeling for Multi-Modal | 130 | "title": "Probabilistic Traffic Motion Labeling for Multi-Modal | ||
131 | Vehicle Route Prediction", | 131 | Vehicle Route Prediction", | ||
132 | "type": "dataset", | 132 | "type": "dataset", | ||
133 | "url": null, | 133 | "url": null, | ||
134 | "version": "" | 134 | "version": "" | ||
135 | } | 135 | } |