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

arXiv: https://arxiv.org/abs/2601.14848 · HTML: https://mlsanity.com/paper/2601.14848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2110.10436/) (2021)
- [Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inference](https://mlsanity.com/paper/2312.06336/) (2023)
- [A human factors approach to validating driver models for interaction-aware automated vehicles](https://mlsanity.com/paper/2109.13077/) (2021)
- [Trajectory-Prediction with Vision: A Survey](https://mlsanity.com/paper/2303.13354/) (2023)
- [Vehicle trajectory prediction in top-view image sequences based on deep learning method](https://mlsanity.com/paper/2102.01749/) (2021)
- [A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems](https://mlsanity.com/paper/2305.01096/) (2023)
- [LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Application](https://mlsanity.com/paper/2305.01095/) (2023)

## Built on by (most cited first, 1 of 1)

- [INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis](https://mlsanity.com/paper/2607.08316/) (2026)

## 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.