Pith. sign in

Paper Citation Record · LEDGER

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics

As of 15 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2507.12083.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.12083 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:00:00.815339Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:27:07.366135Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T06:16:17.936174Z

Reference resolution

48 of 48 outbound references displayed

  • verified exact0
  • verified fuzzy36
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f830a9e6-b09b-4e55-864b-37dfe8e58ba7 · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics nuscenes: A multimodal dataset for autonomous driving

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:06.940326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:54.325345Z digest=sha256:8b6dc74e81eafeafa43a0d8ce7ef4a78f54f2491cefd9ff9fefbe8c1998c759c

Observation 541d6884-8ddf-4aa6-b933-e99431e3a572 · outbound

This paper cites End-to- end object detection with transformers.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics End-to- end object detection with transformers

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:06.799642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:54.389758Z digest=sha256:4debc2ffa2e7429d9b8ed5edf76dd8af1e502a06ec123d20c836f5ba3a584f60

Observation 9e635c8f-2ac5-4687-8221-b84e63ad6944 · outbound

This paper cites MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T16:59:54.485669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:54.485669Z digest=sha256:8e84db61d0e6466192580870a4afe9a9b2016bf179823bf0476ba8c9e7e0a2a8

Observation 649b8f90-9a5f-4ed2-acfb-6e0d78ef3a9c · outbound

This paper cites Argoverse: 3d tracking and forecasting with rich maps.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Argoverse: 3d tracking and forecasting with rich maps

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:06.660100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:54.589564Z digest=sha256:4d8bb80b753a3de392abed745e8fb2a4821204c8b594b3715c1b32b046147fe9

Observation 8f62ca50-39bd-4c27-a547-f5c3dee8de93 · outbound

This paper cites Multimodal trajectory predictions for autonomous driving using deep convolutional networks.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Multimodal trajectory predictions for autonomous driving using deep convolutional networks

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:06.510661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:54.667323Z digest=sha256:9830c38e5d10a89b1467837448269fc0da5f9f3c71bb5036dc3cf408d6e44951

Observation a8a7ad32-c159-415e-b1a7-cad0d9905ba3 · outbound

This paper cites Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T16:59:54.767777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:54.767777Z digest=sha256:42322f939b983dbc0d94c0d2db21048f229a9eb653ef0c63ae5327173d99912e

Observation 5577be57-04df-47ef-9377-7d7580e6be58 · outbound

This paper cites Multimodal trajectory prediction conditioned on lane-graph traversals.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Multimodal trajectory prediction conditioned on lane-graph traversals

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:06.341172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:54.814225Z digest=sha256:01ab3dd7b328822e7d1c695a888f486b0cb4145163addcf44e05eb000c754797

Observation 24e623e4-7257-4e3c-8d67-85110357773a · outbound

This paper cites Mac- former: Map-agent coupled transformer for real-time and ro- bust trajectory prediction.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Mac- former: Map-agent coupled transformer for real-time and ro- bust trajectory prediction

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:06.180289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:54.896628Z digest=sha256:a005634707b597b13224fa15ece89066560ce0b986a0481e2643dddf5e4a36f6

Observation 250a1930-c351-4393-90a7-7843fe75acff · outbound

This paper cites UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T16:59:54.965901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:54.965901Z digest=sha256:3ebc159fe17b7400d05d92e25aded9f2bec47c482d4cc6f8aa700980534a6733

Observation 8b3b3605-dac5-4776-994d-4ca6adad729c · outbound

This paper cites Deep inverse reinforcement learning for be- havior prediction in autonomous driving: Accurate forecasts of vehicle motion.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Deep inverse reinforcement learning for be- havior prediction in autonomous driving: Accurate forecasts of vehicle motion

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:06.043239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:55.066850Z digest=sha256:4884dd4ca758e1c22bfa798cd529bd1b7bb9b69153c3082937d0750214d00231

Observation b81b768d-dce5-462e-9875-48fcede6ae58 · outbound

This paper cites Vectornet: Encoding hd maps and agent dynamics from vectorized rep- resentation.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Vectornet: Encoding hd maps and agent dynamics from vectorized rep- resentation

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:05.918450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:55.138829Z digest=sha256:6794e799678033e10fcead81ba4de6051681884fac9deb4516421cc45d0b2d90

Observation f6d04ac0-5209-4459-b0da-62f115e4c2b1 · outbound

This paper cites THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T16:59:55.216916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:55.216916Z digest=sha256:6e0604913f4bd4aefd75ddc5db1832cd677130d016f4c47266e2c90efe074f3e

Observation 6eca647d-3464-449f-9c73-2395832d8e52 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T16:59:55.480153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:55.480153Z digest=sha256:60a6559f18d16a118ce5439701e11fbcd6fa9303ca81cb225648300ccf1d762a

Observation 2b8dddd1-7d19-4adb-811b-09381c669512 · outbound

This paper cites Densetnt: End-to-end trajectory prediction from dense goal sets.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Densetnt: End-to-end trajectory prediction from dense goal sets

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:05.672560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:55.629987Z digest=sha256:69dab06501ff5d42a4e113fa47c15897e05cefb12504c98dbb47284ead9b1477

Observation 5c9c19c7-9fd6-4096-84d0-c8157255a3c4 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T16:59:55.769470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:55.769470Z digest=sha256:29f03bccb79838fb459d5bd9e76b213a43bb0a24e73c2d051cf019fbc5c8dbaa

Observation 60cf1f9a-dc9c-4fa2-a12b-5f8e65e73320 · outbound

This paper cites Con- ditional predictive behavior planning with inverse reinforce- ment learning for human-like autonomous driving.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Con- ditional predictive behavior planning with inverse reinforce- ment learning for human-like autonomous driving

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:05.361479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:55.866929Z digest=sha256:4131b452001d415871d6ceb83ec889a64605d56f84ec1299b22c47981d063366

Observation 2b12c0cf-a5d8-4b84-b497-596ace650d6b · outbound

This paper cites OpenAI o1 System Card.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics OpenAI o1 System Card

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T16:59:56.293657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:56.293657Z digest=sha256:a3007fcb7fa364a868a070d5529eb7b775601e12c472cc6709609c1cd91e7b25

Observation a895e4a1-f7bb-4305-8b42-68965062ae1b · outbound

This paper cites Learning lane graph representa- tions for motion forecasting.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Learning lane graph representa- tions for motion forecasting

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:05.109500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:56.647028Z digest=sha256:8217b42fe56706b9e7faf0735c0496f81c35522643b3b86ebab2532302f9cc74

Observation 657e6c08-c640-42a0-906f-1507522be6f6 · outbound

This paper cites Kemp: Keyframe-based hierarchical end-to-end deep model for long-term trajectory prediction.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Kemp: Keyframe-based hierarchical end-to-end deep model for long-term trajectory prediction

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:04.874703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:57.437031Z digest=sha256:57660766afd64102afb9d26cf803e5cb3fa599f47f4645a38d9095e96c73e9af

Observation fe7eaf4c-bc54-4eb7-aaf8-856a29707097 · outbound

This paper cites Deep learning-based vehicle behavior prediction for autonomous driving applica- tions: A review.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Deep learning-based vehicle behavior prediction for autonomous driving applica- tions: A review

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:04.681318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:58.283313Z digest=sha256:d1c57d3141dfd829435da1ef2dd3bca7dfdfcc4efae44058cea5b5e341242ad7

Observation c6f569a6-422f-41ae-9269-a68f9a2e178b · outbound

This paper cites Wayformer: Motion forecasting via simple & efficient attention networks.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Wayformer: Motion forecasting via simple & efficient attention networks

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:04.461808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:58.395421Z digest=sha256:af228c8017ae3ef6a323695f0b5dc3ac90366b54cc0cbcfca770d1de8420eb35

Observation 67f50486-9903-4b2b-9cd1-6c1f1f32dcb6 · outbound

This paper cites Algorithms for inverse reinforcement learning.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Algorithms for inverse reinforcement learning

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:04.170870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:58.498024Z digest=sha256:f3efa46f7f3d7d7ee6aaef2d0567de3363878fa3afd460eef3ac500234a47e9f

Observation ab343bcb-c29f-4bcd-b9fb-d5a4cbd41183 · outbound

This paper cites Scene Transformer: A unified architecture for predicting multiple agent trajectories.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Scene Transformer: A unified architecture for predicting multiple agent trajectories

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T16:59:58.555984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:58.555984Z digest=sha256:aa45b01b4554cf38e3764e9503ac837b9190888e62f7be77d85c1d84e0adbc99

Observation ceb74f58-ffbc-46f1-abd8-29784d08233f · outbound

This paper cites Quadruped robot locomotion in unknown terrain using deep reinforcement learning.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Quadruped robot locomotion in unknown terrain using deep reinforcement learning

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:03.867149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:58.653187Z digest=sha256:32e58871c7f04cf3a804a619a50e52cf6a6ef2cde0247c973365f87a93d08c85

Observation 2a8d0dd2-9994-4370-829c-c78c52c531eb · outbound

This paper cites An improved dyna-q algorithm for mobile robot path planning 9 in unknown dynamic environment.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics An improved dyna-q algorithm for mobile robot path planning 9 in unknown dynamic environment

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:03.662096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:58.737101Z digest=sha256:057801082b9dc9fdf971f79cc02349a40e8f1cfbefb6de6cd1ecab3cc087b990

Observation b1087dc3-7802-437c-949c-babc0741cd52 · outbound

This paper cites Sept: Standard-definition map enhanced scene perception and topology reasoning for au- tonomous driving.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Sept: Standard-definition map enhanced scene perception and topology reasoning for au- tonomous driving

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:03.426635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:58.811043Z digest=sha256:b3dcb2774e096dcd260303585cda9dab70be153978fdc619cba86f9c22c6d2f2

Observation 0540f74f-a3e2-425a-88a9-90be2d14807b · outbound

This paper cites Goirl: Graph-oriented inverse reinforcement learning for multimodal trajectory prediction.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Goirl: Graph-oriented inverse reinforcement learning for multimodal trajectory prediction

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:03.320683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:58.881446Z digest=sha256:d7dad71cba32d3c4c813521219a9f6c15db8fced99e35c39842d0c9f6db41cc5

Observation 8309c834-40f0-451f-bc56-d6bc8446f518 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-06T16:59:58.943176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:58.943176Z digest=sha256:a8ca9c726b5fc6407534509dab35c9b125ad27b7404d04cb99c9071a2314bfae

Observation c35cbfd4-fd65-4924-85bd-44685c8ff45b · outbound

This paper cites Scene compliant trajectory forecast with agent- centric spatio-temporal grids.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Scene compliant trajectory forecast with agent- centric spatio-temporal grids

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:03.178482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.026169Z digest=sha256:bddb51970bf9ff4876ee36e49beeabb80a88bd31a029d4ae0202f17129a1f167

Observation 22732677-fc4a-4098-8861-6beee9e1e005 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics U- net: Convolutional networks for biomedical image segmen- tation

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:03.044726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.098916Z digest=sha256:7afe4b79d24bfc88fd759e6807c079cf479c50b23a25c0b6fa14703dba9f8a24

Observation 40741c53-66a8-433c-8828-c6c2a614c8ff · outbound

This paper cites Learning agents for uncertain environments.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Learning agents for uncertain environments

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:02.925492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.141222Z digest=sha256:b8d4ef8c0922a8ea0f68400923ead6c27c2fc0b562ec2cb11b96f0fb21404ce5

Observation 079c421e-22f8-4c62-b323-8cabc4f7b978 · outbound

This paper cites Motion transformer with global intention localization and lo- cal movement refinement.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Motion transformer with global intention localization and lo- cal movement refinement

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:02.813953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.218621Z digest=sha256:8f4241b8633f145c0caf1853523698316f2847ca09f4123ab552826a626d3895

Observation 5a27b50a-fb06-428f-9597-dab12ca9411b · outbound

This paper cites Pip: Planning- informed trajectory prediction for autonomous driving.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Pip: Planning- informed trajectory prediction for autonomous driving

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:02.664382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.301540Z digest=sha256:1d9f1c2ca7cee25d8996477dbbff0394842521bb729425aff654bba79cd10bad

Observation 61e5bc9f-c65f-45d4-8f10-d5e618fc75a5 · outbound

This paper cites Learning to predict vehicle trajectories with model-based planning.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Learning to predict vehicle trajectories with model-based planning

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:02.504280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.363659Z digest=sha256:d8899cf90e2f27a4c2123cc460e3a7ac2207980af1b678153bc929c8f869dab8

Observation 5a4ba55c-7637-431e-aa8b-e0c579d69b9f · outbound

This paper cites Reinforcement learning: An introduction.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Reinforcement learning: An introduction

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:02.451749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.449956Z digest=sha256:1b330a5e58f125b293cee276139f6a89a1a80aa1728814f518f90fff73c9f1a2

Observation 9b463942-9320-4736-b4a4-38279fce15dc · outbound

This paper cites Hpnet: Dynamic trajectory fore- casting with historical prediction attention.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Hpnet: Dynamic trajectory fore- casting with historical prediction attention

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:02.405281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.523657Z digest=sha256:0e9265437cbce975e617ffa856917adc8bbd9795b2d01af179061936eefab607

Observation 3fdf61e2-14fb-411e-8114-7c63fe27487b · outbound

This paper cites Multi- path++: Efficient information fusion and trajectory aggrega- tion for behavior prediction.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Multi- path++: Efficient information fusion and trajectory aggrega- tion for behavior prediction

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:02.384632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.607142Z digest=sha256:1aca78e281872c7aa07b86170789e16aceba3f1e00dbc6842f637c03ecb433a6

Observation e1ee3617-7982-435f-b162-ca2e11fee85f · outbound

This paper cites Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T16:59:59.676544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:59.676544Z digest=sha256:6b57d9a2cd4e7eceeedc1db637ea053d7ea455749deb58e03af29cf6069fc10e

Observation 4ef59b33-cf5f-417f-9108-18d1edbbb728 · outbound

This paper cites Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:02.377436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.768810Z digest=sha256:b8e6f6ff550a99927100fe1ef82d7da1d659e1beb3a4e7428735dd8eeecd6b86

Observation c48364c3-403e-48a4-8a05-7959d9fb7f04 · outbound

This paper cites Large-scale cost function learning for path planning using deep inverse reinforcement learning.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Large-scale cost function learning for path planning using deep inverse reinforcement learning

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:02.352211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:59:59.893221Z digest=sha256:5c30930222156c969fdb4cc2dddf3a0fb3298efdea9c1f80dd22d087c676fa1b

Observation 6872ac3b-5def-4eb9-924b-9e8344e89027 · outbound

This paper cites Goal- lbp: Goal-based local behavior guided trajectory prediction for autonomous driving.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Goal- lbp: Goal-based local behavior guided trajectory prediction for autonomous driving

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:02.161019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T17:00:00.059151Z digest=sha256:2bf57108edc8fda42c2ad3bca20c5137f6eac08bd2dc37174a7ad606e73eb968

Observation dcc889ed-9d77-4de2-a985-69e5862bd262 · outbound

This paper cites DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T17:00:00.163759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:00.163759Z digest=sha256:0594fa69c9abba564c1a9039dfda622fb222d04dfca411e507ba97b2aa689026

Observation 81f069ca-1702-48db-b176-9cd36db8aa78 · outbound

This paper cites Tra- jectory prediction with graph-based dual-scale context fu- sion.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Tra- jectory prediction with graph-based dual-scale context fu- sion

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:01.945674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T17:00:00.230084Z digest=sha256:d7c60c8f2ed7f34c6ffa914bd5c8ef0360c09fa271462e624cee9aae91c52742

Observation d36c8c1b-457e-4ab2-8df4-2bed0efe3735 · outbound

This paper cites Simpl: A simple and efficient multi-agent motion prediction base- line for autonomous driving.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Simpl: A simple and efficient multi-agent motion prediction base- line for autonomous driving

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:01.755075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T17:00:00.346397Z digest=sha256:14714addbdfd992fb92327bbe0f69003b1054680bff9cd83e3837a44a1d33b0d

Observation b9e6e1a4-7419-4685-89e2-8f027a8a8cf7 · outbound

This paper cites Hivt: Hierarchical vector transformer for multi-agent motion prediction.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Hivt: Hierarchical vector transformer for multi-agent motion prediction

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:01.541281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T17:00:00.481482Z digest=sha256:519118307863083e8143af7741b85cb0f7bc788a0d53e846cc983f957ef45b6e

Observation bda39123-5a79-4667-84c9-ae591cbbfa33 · outbound

This paper cites Query-centric trajectory prediction.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Query-centric trajectory prediction

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:01.324109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T17:00:00.596878Z digest=sha256:149e8b080d4ac9d66be1059781dec5b547f8c469d53b12f4b752e05db2c74864

Observation 6db8a691-3257-478a-8857-84aa087b88da · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T17:00:00.721445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:00.721445Z digest=sha256:cdb2a3bbcc86bff1fa28ea9aa7715e84a0d57ea1c34b9e66f2d9f23fff1636f4

Observation 226da59b-b214-4471-ab03-1c1d64b79987 · outbound

This paper cites Maximum entropy inverse reinforcement learning.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics Maximum entropy inverse reinforcement learning

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:01.065973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T17:00:00.815339Z digest=sha256:32824074f650acdc2349290a8d5ce6cdc3ea31b3635f4260596d14e89fd3f14f

Pith citing papers

Observation 9e5c762b-2904-47b5-8663-ee2740047d78 · inbound

A Unified Framework for Trajectory Prediction with Explicit Planning and Reaction Decomposition cites this paper.

A Unified Framework for Trajectory Prediction with Explicit Planning and Reaction Decomposition Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-08-08T04:27:07.599953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-08T04:27:07.366135Z digest=sha256:ce686ea3960d2741469cadeaa6ca7c614d430766b29a24c15da78677b0a7935f