Abstract
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.
Abstract (translated)
随着自动驾驶技术的不断发展，精确轨迹预测模型的重要性变得越来越突出。本文介绍了一种创新模型，将认知洞察力融入轨迹预测中，重点关注感知安全性和动态决策。与传统方法不同，我们的模型在混合自主交通场景中分析互动和行为模式方面表现出色。这标志着一个重大的跃升，在多个关键数据集上取得了显著的性能改进。具体来说，它超越了现有基准，在Next Generation Simulation（NGSIM）数据集上的增益为16.2%，在Highway Drone（HighD）数据集上的增益为27.4%，在Macao Connected Autonomous Driving（MoCAD）数据集上的增益为19.8%。我们提出的模型在处理角点方面表现出卓越的技能，这对于现实世界的应用至关重要。此外，在缺失或有限数据的场景中，其稳健性显然超过了最先进的基准方法。这种适应性和韧性使我们的模型成为现实世界自动驾驶系统的可行工具，为提高安全性和效率预示着一个新的标准。
URL
https://arxiv.org/abs/2404.17520
