用LSTM提前2秒预测车辆过路口意图，准确率超99%。
INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis
- 基于LSTM建模车辆行为序列，预测左转、右转或直行意图。
- 在InD数据集上达到99.71%预测准确率，提前2秒预警。
- 适合自动驾驶系统决策模块，尤其适用于复杂交叉口场景。
车辆意图预测是提升自动驾驶车辆敏捷性与安全性的关键环节。为实现真正的人类级驾驶理解，尤其是在需频繁人机交互的复杂场景（如交叉口、环岛、紧急制动）中，准确预测车辆意图可支持实时避让决策，每一秒的响应都可能避免事故。该研究提出INTENT框架，采用LSTM模型提前2秒预测交叉口车辆的行驶意图（直行、左转、右转），并在InD数据集上通过全面实验与消融分析验证，取得99.71%的预测准确率，显著提升轨迹预测的准确性（意图条件轨迹预测）。
原文摘要 · Abstract (English)
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.
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