中文标题#
MIAT：一种用于时空轨迹预测的机动意图感知 Transformer
英文标题#
MIAT: Maneuver-Intention-Aware Transformer for Spatio-Temporal Trajectory Prediction
中文摘要#
准确的车辆轨迹预测对于安全高效的自动驾驶至关重要，尤其是在混合交通环境中，当人工驾驶车辆和自动驾驶车辆共存时。 然而，由固有的驾驶行为（如加速、减速以及左右变道）引入的不确定性给可靠的轨迹预测带来了重大挑战。 我们引入了一种机动意图感知变压器（MIAT）架构，该架构将机动意图感知控制机制与时空交互建模相结合，以提高长期轨迹预测。 我们系统地研究了不同机动意图意识对短期和长期轨迹预测的影响。 在真实世界的 NGSIM 数据集上评估，并与各种基于变压器和 LSTM 的方法进行比较，我们的方法在短期轨迹预测中比其他意图感知基准方法提高了高达 4.7%，在长期轨迹预测中提高了 1.6%。 此外，通过利用意图感知控制机制，MIAT 在长期轨迹预测中实现了 11.1% 的性能提升，而短期轨迹预测性能略有下降。 源代码和数据集可在https://github.com/cpraskoti/MIAT 获取。
英文摘要#
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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