mlsanityData to 7 Oct 2026

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

arXiv 2601.14848 ↗First posted 21 Jan 2026cs.LG cs.AI cs.NEcited by 1

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Abstract (arXiv)

On and off-ramps are understudied road sections even though they introduce a higher level of variation in highway interactions. Predicting vehicles' behavior in these areas can decrease the impact of uncertainty and increase road safety. In this paper, the difference between this Area of Interest (AoI) and a straight highway section is studied. Multi-layered LSTM architecture to train the AoI model with ExiD drone dataset is utilized. In the process, different prediction horizons and different models' workflow are tested. The results show great promise on horizons up to 4 seconds with prediction accuracy starting from about 76% for the AoI and 94% for the general highway scenarios on the maximum horizon.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 21 Jan 2026.

Builds on

10 of 16 references.

References · 10

  1. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
  2. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
  3. 2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directionscited by 23
  4. 2021A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Drivingcited by 21
  5. 2023Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inferencecited by 7
  6. 2021A human factors approach to validating driver models for interaction-aware automated vehiclescited by 5
  7. 2023Trajectory-Prediction with Vision: A Surveycited by 4
  8. 2021Vehicle trajectory prediction in top-view image sequences based on deep learning methodcited by 2
  9. 2023A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systemscited by 2
  10. 2023LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Applicationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysiscited by 0