中文标题#
MIAT：一種用於時空軌跡預測的機動意圖感知 Transformer
英文标题#
MIAT: Maneuver-Intention-Aware Transformer for Spatio-Temporal Trajectory Prediction
中文摘要#
準確的車輛軌跡預測對於安全高效的自動駕駛至關重要，尤其是在混合交通環境中，當人工駕駛車輛和自動駕駛車輛共存時。 然而，由固有的駕駛行為（如加速、減速以及左右變道）引入的不確定性給可靠的軌跡預測帶來了重大挑戰。 我們引入了一種機動意圖感知變壓器（MIAT）架構，該架構將機動意圖感知控制機制與時空交互建模相結合，以提高長期軌跡預測。 我們系統地研究了不同機動意圖意識對短期和長期軌跡預測的影響。 在真實世界的 NGSIM 數據集上評估，並與各種基於變壓器和 LSTM 的方法進行比較，我們的方法在短期軌跡預測中比其他意圖感知基準方法提高了高達 4.7%，在長期軌跡預測中提高了 1.6%。 此外，通過利用意圖感知控制機制，MIAT 在長期軌跡預測中實現了 11.1% 的性能提升，而短期軌跡預測性能略有下降。 源代碼和數據集可在https://github.com/cpraskoti/MIAT 獲取。
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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