用大模型增强轨迹预测，让自动驾驶更连贯。
PanguMotion: Continuous Driving Motion Forecasting with Pangu Transformers
- 引入Pangu大模型的Transformer模块提升特征表达能力
- 在Argoverse 2数据集上实现连续场景下轨迹预测精度提升
- 适合研究自动驾驶感知与决策的工程师和学者
运动预测是自动驾驶系统的核心任务，旨在准确预测周围交通参与者未来的轨迹以保障行车安全。现有方法通常独立处理离散的驾驶场景，忽略了真实驾驶环境中固有的时间连续性和历史关联性。本文提出PanguMotion，一种面向连续驾驶场景的运动预测框架，将来自Pangu-1B大语言模型的Transformer模块作为特征增强组件，嵌入自动驾驶运动预测架构中。我们在采用RealMotion数据重组织策略处理后的Argoverse 2数据集上进行实验，将每个独立场景转化为连续序列，以模拟真实驾驶环境。
原文摘要 · Abstract (English)
Motion forecasting is a core task in autonomous driving systems, aiming to accurately predict the future trajectories of surrounding agents to ensure driving safety. Existing methods typically process discrete driving scenes independently, neglecting the temporal continuity and historical context correlations inherent in real-world driving environments. This paper proposes PanguMotion, a motion forecasting framework for continuous driving scenarios that integrates Transformer blocks from the Pangu-1B large language model as feature enhancement modules into autonomous driving motion prediction architectures. We conduct experiments on the Argoverse 2 datasets processed by the RealMotion data reorganization strategy, transforming each independent scene into a continuous sequence to mimic real-world driving scenarios.
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