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

Jul 10, 2026

Researchers have proposed INTENT, an LSTM-based framework designed to predict vehicle intentions at intersections up to 2 seconds in advance, classifying actions as going straight, turning left, or turning right. The model achieved 99.71% accuracy on the InD dataset, and comprehensive ablation studies were conducted to demonstrate its effectiveness.

Why it matters: Accurate vehicle intention prediction is critical for autonomous vehicle safety in complex intersection scenarios, potentially preventing collisions and improving decision-making.

Full story at: arXiv AI/ML ↗