MIAT模型通过感知驾驶意图，显著提升复杂交通中车辆轨迹预测精度。
MIAT: Maneuver-Intention-Aware Transformer for Spatio-Temporal Trajectory Prediction
- 引入意图感知控制机制，融合时空交互建模
- 长时预测准确率提升1.6%，最高达11.1%性能增益
- 适合自动驾驶决策与交通流仿真场景
精准的车辆轨迹预测对安全高效的自动驾驶至关重要，尤其在人类驾驶与自动驾驶车辆共存的混合交通环境中。然而，加速、减速及左右变道等固有驾驶行为带来的不确定性，给可靠轨迹预测带来巨大挑战。本文提出一种意图感知的时空变换器（MIAT）架构，将变道意图感知控制机制与时空交互建模相结合，以增强长时轨迹预测能力。系统研究了不同意图感知程度对短时与长时轨迹预测的影响。在真实世界NGSIM数据集上评估，并与多种基于Transformer和LSTM的方法对比，本方法在短时预测上相比其他意图感知基准方法提升最高达4.7%，长时预测提升1.6%。此外，通过引入意图感知控制机制，MIAT在长时预测上实现11.1%的性能提升，短时性能略有下降。源代码与数据集见https://github.com/cpraskoti/MIAT。
原文摘要 · Abstract (English)
Accurate vehicle trajectory prediction is critical for safe and efficient autonomous driving, especially in mixed traffic environments when both human-driven and autonomous vehicles co-exist. However, uncertainties introduced by inherent driving behaviors -- such as acceleration, deceleration, and left and right maneuvers -- pose significant challenges for reliable trajectory prediction. We introduce a Maneuver-Intention-Aware Transformer (MIAT) architecture, which integrates a maneuver intention awareness control mechanism with spatiotemporal interaction modeling to enhance long-horizon trajectory predictions. We systematically investigate the impact of varying awareness of maneuver intention on both short- and long-horizon trajectory predictions. Evaluated on the real-world NGSIM dataset and benchmarked against various transformer- and LSTM-based methods, our approach achieves an improvement of up to 4.7% in short-horizon predictions and a 1.6% in long-horizon predictions compared to other intention-aware benchmark methods. Moreover, by leveraging intention awareness control mechanism, MIAT realizes an 11.1% performance boost in long-horizon predictions, with a modest drop in short-horizon performance. The source code and datasets are available at https://github.com/cpraskoti/MIAT.
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