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This study introduces the INTENT framework, leveraging an LSTM model to predict vehicle intentions at intersections two seconds prior to events, specifically identifying whether vehicles will go straight, turn left, or turn right. The research addresses the critical need for autonomous vehicles to interpret human driving intentions, particularly in complex scenarios that require rapid decision-making to enhance safety. The model achieved an impressive accuracy of 99.71% on the InD dataset, demonstrating its potential to significantly improve the agility and safety of autonomous driving systems.
Predicting vehicle intentions with 99.71% accuracy could redefine safety protocols for autonomous vehicles in complex driving scenarios.
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.
