FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding Article Swipe
Mingkun Wang
,
Xiaoguang Ren
,
Ruochun Jin
,
Minglong Li
,
Xiaochuan Zhang
,
Changqian Yu
,
Mingxu Wang
,
Wenjing Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.14422
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.14422
Most prior motion prediction endeavors in autonomous driving have inadequately encoded future scenarios, leading to predictions that may fail to accurately capture the diverse movements of agents (e.g., vehicles or pedestrians). To address this, we propose FutureNet, which explicitly integrates initially predicted trajectories into the future scenario and further encodes these future contexts to enhance subsequent forecasting. Additionally, most previous motion forecasting works have focused on predicting independent futures for each agent. However, safe and smooth autonomous driving requires accurately predicting the diverse future behaviors of numerous surrounding agents jointly in complex dynamic environments. Given that all agents occupy certain potential travel spaces and possess lane driving priority, we propose Lane Occupancy Field (LOF), a new representation with lane semantics for motion forecasting in autonomous driving. LOF can simultaneously capture the joint probability distribution of all road participants' future spatial-temporal positions. Due to the high compatibility between lane occupancy field prediction and trajectory prediction, we propose a novel network with future context encoding for the joint prediction of these two tasks. Our approach ranks 1st on two large-scale motion forecasting benchmarks: Argoverse 1 and Argoverse 2.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.14422
- https://arxiv.org/pdf/2406.14422
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399911844
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399911844Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2406.14422Digital Object Identifier
- Title
-
FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context EncodingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-20Full publication date if available
- Authors
-
Mingkun Wang, Xiaoguang Ren, Ruochun Jin, Minglong Li, Xiaochuan Zhang, Changqian Yu, Mingxu Wang, Wenjing YangList of authors in order
- Landing page
-
https://arxiv.org/abs/2406.14422Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2406.14422Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2406.14422Direct OA link when available
- Concepts
-
Trajectory, Occupancy, Encoding (memory), Context (archaeology), Computer science, Joint (building), Field (mathematics), Artificial intelligence, Mathematics, Engineering, Geography, Physics, Structural engineering, Archaeology, Astronomy, Pure mathematics, Architectural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.capture | 21, 129 |
| abstract_inverted_index.certain | 99 |
| abstract_inverted_index.complex | 91 |
| abstract_inverted_index.context | 161 |
| abstract_inverted_index.diverse | 23, 82 |
| abstract_inverted_index.driving | 7, 77, 106 |
| abstract_inverted_index.dynamic | 92 |
| abstract_inverted_index.encoded | 10 |
| abstract_inverted_index.encodes | 49 |
| abstract_inverted_index.enhance | 54 |
| abstract_inverted_index.focused | 64 |
| abstract_inverted_index.further | 48 |
| abstract_inverted_index.futures | 68 |
| abstract_inverted_index.jointly | 89 |
| abstract_inverted_index.leading | 13 |
| abstract_inverted_index.network | 158 |
| abstract_inverted_index.possess | 104 |
| abstract_inverted_index.propose | 35, 109, 155 |
| abstract_inverted_index.However, | 72 |
| abstract_inverted_index.approach | 172 |
| abstract_inverted_index.contexts | 52 |
| abstract_inverted_index.driving. | 125 |
| abstract_inverted_index.encoding | 162 |
| abstract_inverted_index.numerous | 86 |
| abstract_inverted_index.previous | 59 |
| abstract_inverted_index.requires | 78 |
| abstract_inverted_index.scenario | 46 |
| abstract_inverted_index.vehicles | 28 |
| abstract_inverted_index.Argoverse | 181, 184 |
| abstract_inverted_index.Occupancy | 111 |
| abstract_inverted_index.behaviors | 84 |
| abstract_inverted_index.endeavors | 4 |
| abstract_inverted_index.initially | 40 |
| abstract_inverted_index.movements | 24 |
| abstract_inverted_index.occupancy | 148 |
| abstract_inverted_index.potential | 100 |
| abstract_inverted_index.predicted | 41 |
| abstract_inverted_index.priority, | 107 |
| abstract_inverted_index.semantics | 119 |
| abstract_inverted_index.FutureNet, | 36 |
| abstract_inverted_index.accurately | 20, 79 |
| abstract_inverted_index.autonomous | 6, 76, 124 |
| abstract_inverted_index.explicitly | 38 |
| abstract_inverted_index.integrates | 39 |
| abstract_inverted_index.positions. | 140 |
| abstract_inverted_index.predicting | 66, 80 |
| abstract_inverted_index.prediction | 3, 150, 166 |
| abstract_inverted_index.scenarios, | 12 |
| abstract_inverted_index.subsequent | 55 |
| abstract_inverted_index.trajectory | 152 |
| abstract_inverted_index.benchmarks: | 180 |
| abstract_inverted_index.forecasting | 61, 122, 179 |
| abstract_inverted_index.independent | 67 |
| abstract_inverted_index.large-scale | 177 |
| abstract_inverted_index.prediction, | 153 |
| abstract_inverted_index.predictions | 15 |
| abstract_inverted_index.probability | 132 |
| abstract_inverted_index.surrounding | 87 |
| abstract_inverted_index.distribution | 133 |
| abstract_inverted_index.forecasting. | 56 |
| abstract_inverted_index.inadequately | 9 |
| abstract_inverted_index.trajectories | 42 |
| abstract_inverted_index.Additionally, | 57 |
| abstract_inverted_index.compatibility | 145 |
| abstract_inverted_index.environments. | 93 |
| abstract_inverted_index.participants' | 137 |
| abstract_inverted_index.pedestrians). | 30 |
| abstract_inverted_index.representation | 116 |
| abstract_inverted_index.simultaneously | 128 |
| abstract_inverted_index.spatial-temporal | 139 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |