# INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis

arXiv: https://arxiv.org/abs/2607.08316 · HTML: https://mlsanity.com/paper/2607.08316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 9 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inference](https://mlsanity.com/paper/2312.06336/) (2023)
- [Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection](https://mlsanity.com/paper/2006.05918/) (2020)
- [Real-World Deployment of a Lane Change Prediction Architecture Based on Knowledge Graph Embeddings and Bayesian Inference](https://mlsanity.com/paper/2506.11925/) (2025)
- [VIT-Ped: Visionary Intention Transformer for Pedestrian Behavior Analysis](https://mlsanity.com/paper/2601.01989/) (2026)
- [Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information](https://mlsanity.com/paper/2301.02561/) (2023)
- [Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction](https://mlsanity.com/paper/2509.20218/) (2025)
- [From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps](https://mlsanity.com/paper/2601.14848/) (2026)

## Abstract (arXiv)

Vehicle intention prediction is a pivotal aspect in the agility and safety of autonomous vehicles in all driving scenarios; if genuine enhancement of autonomous vehicles are required, we need to make them adopt human interpretation of driver's intention especially in cases that require a lot of human interaction as well as complex driving behaviors like the ones at intersections, roundabouts and emergency cases such as sudden stops where vehicle intention prediction helps in taking the correct evasive action within a real time period where every second of action makes an impact and can prevent a catastrophe from taking place. In the worst case, it helps minimize the damage and make safety a priority. Intention prediction can also be used to enhance trajectory prediction (intention conditioned trajectory prediction). In this study, The INTENT framework is proposed using LSTM model to predict the vehicle's intention at intersections 2 seconds ahead of the event occurrence to predict whether the cars in intersections are going straight, turning left, or turning right. Various model experiments and ablation study are thoroughly tested on InD dataset achieving 99.71% accuracy.