Paper Citation Record · LEDGER

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps

As of 11 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2601.14848.

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

pith.paper-citation-record.v1
2601.14848 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-21T16:03:02.705191Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

verified exact3
verified fuzzy13
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5e742009-6555-4316-b696-6b641ea65e1e · outbound

This paper cites More than a million people die from road injuries every year.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps More than a million people die from road injuries every year

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.834710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 204f2e3b-e4ec-495c-8e3a-822b875e29de · outbound

This paper cites Road traffic injuries.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps Road traffic injuries

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.808919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 69667960-ea3f-451c-9e30-4392c17ab2a7 · outbound

This paper cites Machine learning for autonomous vehi- cle’s trajectory prediction: A comprehensive survey, challenges, and future research directions.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps Machine learning for autonomous vehi- cle’s trajectory prediction: A comprehensive survey, challenges, and future research directions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.810885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 819ed055-f55d-46d4-9822-a2a148762e2d · outbound

This paper cites An enhanced vehicle trajectory prediction model leveraging lstm and social-attention mechanisms.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps An enhanced vehicle trajectory prediction model leveraging lstm and social-attention mechanisms

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.817117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 49e3a84c-6470-4840-b632-cd3ba2ad868b · outbound

This paper cites Vehicle lane change prediction based on knowledge graph embeddings and bayesian inference.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps Vehicle lane change prediction based on knowledge graph embeddings and bayesian inference

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.814991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 12360b52-7450-4ae1-be55-d5c83fdd06a0 · outbound

This paper cites Vehicle trajectory prediction in top-view image sequences based on deep learning method.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps Vehicle trajectory prediction in top-view image sequences based on deep learning method

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-21T16:04:14.650139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 758ee487-7f6e-464c-987e-dfa79ad4fd49 · outbound

This paper cites Trajectory-prediction with vision: A survey.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps Trajectory-prediction with vision: A survey

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.826008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3e21c728-a100-47f1-9968-aa0a23d3fb56 · outbound

This paper cites A review of deep learning-based vehicle motion prediction for autonomous driving.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps A review of deep learning-based vehicle motion prediction for autonomous driving

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.812984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ffb8c2ed-13f3-4d91-8972-8cb158c79c97 · outbound

This paper cites A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-21T16:04:14.646470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c899bdb5-323b-4b5b-858e-17650c2fb6ea · outbound

This paper cites Artificial intelligence techniques for driving safety and vehicle crash prediction.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps Artificial intelligence techniques for driving safety and vehicle crash prediction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.832666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 11492656-ab95-4038-a8aa-7c8495ef49d6 · outbound

This paper cites INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-21T16:04:14.642691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a0b47b06-7bd4-4e40-a8a5-999121f4c239 · outbound

This paper cites A human factors approach to validating driver models for interaction-aware automated vehicles.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps A human factors approach to validating driver models for interaction-aware automated vehicles

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.824128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation efd42011-0c04-4d27-a1a4-3a061fdd8fe2 · outbound

This paper cites Lstm-based preceding vehicle behaviour prediction during aggressive lane change for acc application.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps Lstm-based preceding vehicle behaviour prediction during aggressive lane change for acc application

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.828000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f79a6e34-98a8-4a4f-b589-7d97c45aa513 · outbound

This paper cites A novel model for driver lane change prediction in cooperative adaptive cruise control systems.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps A novel model for driver lane change prediction in cooperative adaptive cruise control systems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.819866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b15fa294-e514-49a5-9888-876dd202c24b · outbound

This paper cites The exid dataset: A real-world trajectory dataset of highly interactive highway scenarios in germany.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps The exid dataset: A real-world trajectory dataset of highly interactive highway scenarios in germany

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.830330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8e388dc7-abea-4549-9134-f07461ceb9d9 · outbound

This paper cites The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems.

From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T16:04:14.822241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.