As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and dynamic decision-making. Distinct from traditional approaches, our model excels in analyzing interactions and behavior patterns in mixed autonomy traffic scenarios. It represents a significant leap forward, achieving marked performance improvements on several key datasets. Specifically, it surpasses existing benchmarks with gains of 16.2% on the Next Generation Simulation (NGSIM), 27.4% on the Highway Drone (HighD), and 19.8% on the Macao Connected Autonomous Driving (MoCAD) dataset. Our proposed model shows exceptional proficiency in handling corner cases, essential for real-world applications. Moreover, its robustness is evident in scenarios with missing or limited data, outperforming most of the state-of-the-art baselines. This adaptability and resilience position our model as a viable tool for real-world autonomous driving systems, heralding a new standard in vehicle trajectory prediction for enhanced safety and efficiency.

翻译：随着自动驾驶技术的发展，精确的轨迹预测模型变得至关重要。本文提出了一种融合认知洞察力的创新轨迹预测模型，聚焦于感知安全与动态决策。与传统方法不同，该模型在分析混合自主交通场景中的交互与行为模式方面表现出色，实现了显著性能突破。具体而言，该模型在三个关键数据集上均超越了现有基准：在下一代仿真（NGSIM）数据集上提升16.2%，在高速公路无人机（HighD）数据集上提升27.4%，在澳门网联自动驾驶（MoCAD）数据集上提升19.8%。所提模型在处理对实际应用至关重要的边缘案例时展现出卓越能力。此外，其在数据缺失或受限场景下的鲁棒性优于多数先进基线模型。这种适应性与稳健性使其成为实际自动驾驶系统的可行工具，为提升车辆轨迹预测的安全性与效率树立了新标准。