TNT: Target-driveN Trajectory Prediction Article Swipe
Hang Zhao
,
Jiyang Gao
,
Tian Lan
,
Chen Sun
,
Benjamin Sapp
,
Balakrishnan Varadarajan
,
Yue Shen
,
Yi Shen
,
Yuning Chai
,
Cordelia Schmid
,
Congcong Li
,
Dragomir Anguelov
·
YOU?
·
· 2020
· Open Access
·
YOU?
·
· 2020
· Open Access
·
Predicting the future behavior of moving agents is essential for real world applications. It is challenging as the intent of the agent and the corresponding behavior is unknown and intrinsically multimodal. Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states. This leads to our target-driven trajectory prediction (TNT) framework. TNT has three stages which are trained end-to-end. It first predicts an agent's potential target states $T$ steps into the future, by encoding its interactions with the environment and the other agents. TNT then generates trajectory state sequences conditioned on targets. A final stage estimates trajectory likelihoods and a final compact set of trajectory predictions is selected. This is in contrast to previous work which models agent intents as latent variables, and relies on test-time sampling to generate diverse trajectories. We benchmark TNT on trajectory prediction of vehicles and pedestrians, where we outperform state-of-the-art on Argoverse Forecasting, INTERACTION, Stanford Drone and an in-house Pedestrian-at-Intersection dataset.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://arxiv.org/pdf/2008.08294
- OA Status
- green
- Cited By
- 13
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3207755341
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3207755341Canonical identifier for this work in OpenAlex
- Title
-
TNT: Target-driveN Trajectory PredictionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-19Full publication date if available
- Authors
-
Hang Zhao, Jiyang Gao, Tian Lan, Chen Sun, Benjamin Sapp, Balakrishnan Varadarajan, Yue Shen, Yi Shen, Yuning Chai, Cordelia Schmid, Congcong Li, Dragomir AnguelovList of authors in order
- Landing page
-
https://arxiv.org/pdf/2008.08294Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2008.08294Direct OA link when available
- Concepts
-
Trajectory, Benchmark (surveying), Computer science, Set (abstract data type), Intersection (aeronautics), Key (lock), Artificial intelligence, Machine learning, Engineering, Aerospace engineering, Astronomy, Geography, Geodesy, Programming language, Physics, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 5, 2021: 6, 2020: 2Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3207755341 |
|---|---|
| doi | |
| ids.mag | 3207755341 |
| ids.openalex | https://openalex.org/W3207755341 |
| fwci | 1.38918702 |
| type | article |
| title | TNT: Target-driveN Trajectory Prediction |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 904 |
| biblio.first_page | 895 |
| topics[0].id | https://openalex.org/T11099 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9997000098228455 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2203 |
| topics[0].subfield.display_name | Automotive Engineering |
| topics[0].display_name | Autonomous Vehicle Technology and Safety |
| topics[1].id | https://openalex.org/T10331 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9987999796867371 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Video Surveillance and Tracking Methods |
| topics[2].id | https://openalex.org/T11512 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9950000047683716 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Anomaly Detection Techniques and Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C13662910 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8779627084732056 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q193139 |
| concepts[0].display_name | Trajectory |
| concepts[1].id | https://openalex.org/C185798385 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7398046255111694 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[1].display_name | Benchmark (surveying) |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6471458077430725 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C177264268 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5501986145973206 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[3].display_name | Set (abstract data type) |
| concepts[4].id | https://openalex.org/C64543145 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5495315194129944 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q162942 |
| concepts[4].display_name | Intersection (aeronautics) |
| concepts[5].id | https://openalex.org/C26517878 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5445483326911926 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q228039 |
| concepts[5].display_name | Key (lock) |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.49085432291030884 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.43103379011154175 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C127413603 |
| concepts[8].level | 0 |
| concepts[8].score | 0.1358291506767273 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[8].display_name | Engineering |
| concepts[9].id | https://openalex.org/C146978453 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[9].display_name | Aerospace engineering |
| concepts[10].id | https://openalex.org/C1276947 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q333 |
| concepts[10].display_name | Astronomy |
| concepts[11].id | https://openalex.org/C205649164 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[11].display_name | Geography |
| concepts[12].id | https://openalex.org/C13280743 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q131089 |
| concepts[12].display_name | Geodesy |
| concepts[13].id | https://openalex.org/C199360897 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[13].display_name | Programming language |
| concepts[14].id | https://openalex.org/C121332964 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[14].display_name | Physics |
| concepts[15].id | https://openalex.org/C38652104 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[15].display_name | Computer security |
| keywords[0].id | https://openalex.org/keywords/trajectory |
| keywords[0].score | 0.8779627084732056 |
| keywords[0].display_name | Trajectory |
| keywords[1].id | https://openalex.org/keywords/benchmark |
| keywords[1].score | 0.7398046255111694 |
| keywords[1].display_name | Benchmark (surveying) |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6471458077430725 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/set |
| keywords[3].score | 0.5501986145973206 |
| keywords[3].display_name | Set (abstract data type) |
| keywords[4].id | https://openalex.org/keywords/intersection |
| keywords[4].score | 0.5495315194129944 |
| keywords[4].display_name | Intersection (aeronautics) |
| keywords[5].id | https://openalex.org/keywords/key |
| keywords[5].score | 0.5445483326911926 |
| keywords[5].display_name | Key (lock) |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.49085432291030884 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.43103379011154175 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/engineering |
| keywords[8].score | 0.1358291506767273 |
| keywords[8].display_name | Engineering |
| language | en |
| locations[0].id | mag:3207755341 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | arXiv (Cornell University) |
| locations[0].landing_page_url | https://arxiv.org/pdf/2008.08294 |
| authorships[0].author.id | https://openalex.org/A5101826599 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0648-9823 |
| authorships[0].author.display_name | Hang Zhao |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hang Zhao |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5104212216 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Jiyang Gao |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jiyang Gao |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5018464968 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3010-8090 |
| authorships[2].author.display_name | Tian Lan |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Tian Lan |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5100722237 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4142-4008 |
| authorships[3].author.display_name | Chen Sun |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Chen Sun |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5014258241 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Benjamin Sapp |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Benjamin Sapp |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5114118962 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Balakrishnan Varadarajan |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Balakrishnan Varadarajan |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5066918650 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-7338-6045 |
| authorships[6].author.display_name | Yue Shen |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Yue Shen |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5045434740 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-1679-9238 |
| authorships[7].author.display_name | Yi Shen |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Yi Shen |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5079961895 |
| authorships[8].author.orcid | |
| authorships[8].author.display_name | Yuning Chai |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Yuning Chai |
| authorships[8].is_corresponding | False |
| authorships[9].author.id | https://openalex.org/A5109890544 |
| authorships[9].author.orcid | |
| authorships[9].author.display_name | Cordelia Schmid |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Cordelia Schmid |
| authorships[9].is_corresponding | False |
| authorships[10].author.id | https://openalex.org/A5100332246 |
| authorships[10].author.orcid | https://orcid.org/0000-0003-4633-4391 |
| authorships[10].author.display_name | Congcong Li |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Congcong Li |
| authorships[10].is_corresponding | False |
| authorships[11].author.id | https://openalex.org/A5081024054 |
| authorships[11].author.orcid | |
| authorships[11].author.display_name | Dragomir Anguelov |
| authorships[11].author_position | last |
| authorships[11].raw_author_name | Dragomir Anguelov |
| authorships[11].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2008.08294 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | TNT: Target-driveN Trajectory Prediction |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-10-10T17:16:08.811792 |
| primary_topic.id | https://openalex.org/T11099 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9997000098228455 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2203 |
| primary_topic.subfield.display_name | Automotive Engineering |
| primary_topic.display_name | Autonomous Vehicle Technology and Safety |
| related_works | https://openalex.org/W3062588417, https://openalex.org/W2424778531, https://openalex.org/W3035671534, https://openalex.org/W3028769608, https://openalex.org/W2963001155, https://openalex.org/W3108486966, https://openalex.org/W2967177252, https://openalex.org/W2894978157, https://openalex.org/W3108908812, https://openalex.org/W3035574168, https://openalex.org/W2983397630, https://openalex.org/W2962687116, https://openalex.org/W2607296803, https://openalex.org/W3208044169, https://openalex.org/W3134623788, https://openalex.org/W3125605478, https://openalex.org/W3116651890, https://openalex.org/W3034722190, https://openalex.org/W2997264916, https://openalex.org/W2970116586 |
| cited_by_count | 13 |
| counts_by_year[0].year | 2022 |
| counts_by_year[0].cited_by_count | 5 |
| counts_by_year[1].year | 2021 |
| counts_by_year[1].cited_by_count | 6 |
| counts_by_year[2].year | 2020 |
| counts_by_year[2].cited_by_count | 2 |
| locations_count | 1 |
| best_oa_location.id | mag:3207755341 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | arXiv (Cornell University) |
| best_oa_location.landing_page_url | https://arxiv.org/pdf/2008.08294 |
| primary_location.id | mag:3207755341 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | arXiv (Cornell University) |
| primary_location.landing_page_url | https://arxiv.org/pdf/2008.08294 |
| publication_date | 2020-08-19 |
| publication_year | 2020 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 106 |
| abstract_inverted_index.a | 39, 51, 113 |
| abstract_inverted_index.It | 13, 73 |
| abstract_inverted_index.We | 145 |
| abstract_inverted_index.an | 76, 166 |
| abstract_inverted_index.as | 16, 133 |
| abstract_inverted_index.be | 47 |
| abstract_inverted_index.by | 50, 86 |
| abstract_inverted_index.in | 124 |
| abstract_inverted_index.is | 7, 14, 26, 34, 120, 123 |
| abstract_inverted_index.of | 4, 19, 53, 117, 151 |
| abstract_inverted_index.on | 104, 138, 148, 159 |
| abstract_inverted_index.to | 58, 126, 141 |
| abstract_inverted_index.we | 156 |
| abstract_inverted_index.$T$ | 81 |
| abstract_inverted_index.Our | 31 |
| abstract_inverted_index.TNT | 65, 97, 147 |
| abstract_inverted_index.and | 22, 28, 93, 112, 136, 153, 165 |
| abstract_inverted_index.are | 70 |
| abstract_inverted_index.can | 46 |
| abstract_inverted_index.for | 9, 36 |
| abstract_inverted_index.has | 66 |
| abstract_inverted_index.its | 88 |
| abstract_inverted_index.key | 32 |
| abstract_inverted_index.our | 59 |
| abstract_inverted_index.set | 52, 116 |
| abstract_inverted_index.the | 1, 17, 20, 23, 43, 84, 91, 94 |
| abstract_inverted_index.This | 56, 122 |
| abstract_inverted_index.into | 83 |
| abstract_inverted_index.real | 10 |
| abstract_inverted_index.that | 35 |
| abstract_inverted_index.then | 98 |
| abstract_inverted_index.time | 41 |
| abstract_inverted_index.with | 90 |
| abstract_inverted_index.work | 128 |
| abstract_inverted_index.(TNT) | 63 |
| abstract_inverted_index.Drone | 164 |
| abstract_inverted_index.agent | 21, 131 |
| abstract_inverted_index.final | 107, 114 |
| abstract_inverted_index.first | 74 |
| abstract_inverted_index.leads | 57 |
| abstract_inverted_index.modes | 45 |
| abstract_inverted_index.other | 95 |
| abstract_inverted_index.stage | 108 |
| abstract_inverted_index.state | 101 |
| abstract_inverted_index.steps | 82 |
| abstract_inverted_index.three | 67 |
| abstract_inverted_index.where | 155 |
| abstract_inverted_index.which | 69, 129 |
| abstract_inverted_index.world | 11 |
| abstract_inverted_index.agents | 6 |
| abstract_inverted_index.future | 2, 44 |
| abstract_inverted_index.intent | 18 |
| abstract_inverted_index.latent | 134 |
| abstract_inverted_index.models | 130 |
| abstract_inverted_index.moving | 5 |
| abstract_inverted_index.relies | 137 |
| abstract_inverted_index.stages | 68 |
| abstract_inverted_index.states | 80 |
| abstract_inverted_index.target | 54, 79 |
| abstract_inverted_index.within | 38 |
| abstract_inverted_index.agent's | 77 |
| abstract_inverted_index.agents. | 96 |
| abstract_inverted_index.compact | 115 |
| abstract_inverted_index.diverse | 143 |
| abstract_inverted_index.future, | 85 |
| abstract_inverted_index.insight | 33 |
| abstract_inverted_index.intents | 132 |
| abstract_inverted_index.states. | 55 |
| abstract_inverted_index.trained | 71 |
| abstract_inverted_index.unknown | 27 |
| abstract_inverted_index.Stanford | 163 |
| abstract_inverted_index.behavior | 3, 25 |
| abstract_inverted_index.captured | 49 |
| abstract_inverted_index.contrast | 125 |
| abstract_inverted_index.dataset. | 169 |
| abstract_inverted_index.encoding | 87 |
| abstract_inverted_index.generate | 142 |
| abstract_inverted_index.horizon, | 42 |
| abstract_inverted_index.in-house | 167 |
| abstract_inverted_index.moderate | 40 |
| abstract_inverted_index.predicts | 75 |
| abstract_inverted_index.previous | 127 |
| abstract_inverted_index.sampling | 140 |
| abstract_inverted_index.targets. | 105 |
| abstract_inverted_index.vehicles | 152 |
| abstract_inverted_index.Argoverse | 160 |
| abstract_inverted_index.benchmark | 146 |
| abstract_inverted_index.essential | 8 |
| abstract_inverted_index.estimates | 109 |
| abstract_inverted_index.generates | 99 |
| abstract_inverted_index.potential | 78 |
| abstract_inverted_index.selected. | 121 |
| abstract_inverted_index.sequences | 102 |
| abstract_inverted_index.test-time | 139 |
| abstract_inverted_index.Predicting | 0 |
| abstract_inverted_index.framework. | 64 |
| abstract_inverted_index.outperform | 157 |
| abstract_inverted_index.prediction | 37, 62, 150 |
| abstract_inverted_index.trajectory | 61, 100, 110, 118, 149 |
| abstract_inverted_index.variables, | 135 |
| abstract_inverted_index.challenging | 15 |
| abstract_inverted_index.conditioned | 103 |
| abstract_inverted_index.effectively | 48 |
| abstract_inverted_index.end-to-end. | 72 |
| abstract_inverted_index.environment | 92 |
| abstract_inverted_index.likelihoods | 111 |
| abstract_inverted_index.multimodal. | 30 |
| abstract_inverted_index.predictions | 119 |
| abstract_inverted_index.Forecasting, | 161 |
| abstract_inverted_index.INTERACTION, | 162 |
| abstract_inverted_index.interactions | 89 |
| abstract_inverted_index.pedestrians, | 154 |
| abstract_inverted_index.applications. | 12 |
| abstract_inverted_index.corresponding | 24 |
| abstract_inverted_index.intrinsically | 29 |
| abstract_inverted_index.target-driven | 60 |
| abstract_inverted_index.trajectories. | 144 |
| abstract_inverted_index.state-of-the-art | 158 |
| abstract_inverted_index.Pedestrian-at-Intersection | 168 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.80651473 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |