Änderungen
Auf 24. November 2023 um 13:36:42 UTC, Felix Fröhling:
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Die neue Ressource Article wurde zu Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction hinzugefügt
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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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36 | "maintainer": "[{\"Maintainer Email\": | 36 | "maintainer": "[{\"Maintainer Email\": | ||
37 | \"eduardo.sanchezmorales@thi.de\", \"maintainer\": \"Alberto Flores | 37 | \"eduardo.sanchezmorales@thi.de\", \"maintainer\": \"Alberto Flores | ||
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n | 41 | "metadata_modified": "2023-11-24T13:34:20.152563", | n | 41 | "metadata_modified": "2023-11-24T13:36:42.851482", |
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.", | ||
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76 | "approval_status": "approved", | 76 | "approval_status": "approved", | ||
77 | "created": "2023-06-27T14:06:24.055358", | 77 | "created": "2023-06-27T14:06:24.055358", | ||
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103 | "title": "Probabilistic Traffic Motion Labeling for Multi-Modal | 130 | "title": "Probabilistic Traffic Motion Labeling for Multi-Modal | ||
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