DEMO: A Dynamics-Enhanced Learning Model for multi-horizon trajectory prediction in autonomous vehicles

IF 14.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Information Fusion Pub Date : 2025-01-09 DOI:10.1016/j.inffus.2024.102924
Chengyue Wang, Haicheng Liao, Kaiqun Zhu, Guohui Zhang, Zhenning Li
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Abstract

Autonomous vehicles (AVs) rely on accurate trajectory prediction of surrounding vehicles to ensure the safety of both passengers and other road users. Trajectory prediction spans both short-term and long-term horizons, each requiring distinct considerations: short-term predictions rely on accurately capturing the vehicle’s dynamics, while long-term predictions rely on accurately modeling the interaction patterns within the environment. However current approaches, either physics-based or learning-based models, always ignore these distinct considerations, making them struggle to find the optimal prediction for both short-term and long-term horizon. In this paper, we introduce the Dynamics-Enhanced Learning MOdel (DEMO), a novel approach that combines a physics-based Vehicle Dynamics Model with advanced deep learning algorithms. DEMO employs a two-stage architecture, featuring a Dynamics Learning Stage and an Interaction Learning Stage, where the former stage focuses on capturing vehicle motion dynamics and the latter focuses on modeling interaction. By capitalizing on the respective strengths of both methods, DEMO facilitates multi-horizon predictions for future trajectories. Experimental results on the Next Generation Simulation (NGSIM), Macau Connected Autonomous Driving (MoCAD), Highway Drone (HighD), and nuScenes datasets demonstrate that DEMO outperforms state-of-the-art (SOTA) baselines in both short-term and long-term prediction horizons.
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演示:用于自动驾驶车辆多视界轨迹预测的动态增强学习模型
自动驾驶汽车依靠对周围车辆的准确轨迹预测来确保乘客和其他道路使用者的安全。轨迹预测跨越短期和长期视野,每一个都需要不同的考虑:短期预测依赖于准确捕捉车辆的动态,而长期预测依赖于准确模拟环境中的相互作用模式。然而,目前的方法,无论是基于物理的还是基于学习的模型,总是忽略了这些不同的考虑因素,这使得它们很难找到短期和长期的最佳预测。在本文中,我们介绍了动态增强学习模型(DEMO),这是一种将基于物理的车辆动力学模型与先进的深度学习算法相结合的新方法。DEMO采用两阶段架构,包括动力学学习阶段和交互学习阶段,其中前一阶段侧重于捕获车辆运动动力学,后一阶段侧重于建模交互。通过利用这两种方法各自的优势,DEMO促进了对未来轨迹的多视界预测。在下一代模拟(NGSIM)、澳门互联自动驾驶(MoCAD)、高速公路无人机(HighD)和nuScenes数据集上的实验结果表明,DEMO在短期和长期预测范围内都优于最先进的(SOTA)基线。
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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