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

2026-07-09

AI Summary

Accurately predicting the driving intentions—such as going straight, turning left, or turning right—of surrounding vehicles in complex intersection scenarios is critical for enhancing the safety and decision-making capabilities of autonomous driving systems. This work proposes the INTENT framework, which achieves high-accuracy intention prediction on the real-world Intersection Dataset (InD) for the first time. By leveraging LSTM networks to model temporal driving behaviors, the framework attains a prediction accuracy of 99.71% within a 2-second forecasting horizon. Systematic ablation studies validate the contribution of each module to overall performance, demonstrating a significant improvement in the autonomous system’s responsiveness in highly interactive traffic environments.

Abstract

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.

Problem

• vehicle intention prediction
• autonomous vehicles
• intersection scenarios
• trajectory prediction
• real-time decision-making

Innovation

• vehicle intention prediction
• LSTM
• intersection scenarios
• ablation analysis
• autonomous driving