基于学习的轨道预测模型已经取得了巨大的成功，除了运动历史外，还可以利用情境信息。然而，我们发现，最先进的预测方法往往过度依赖于代理的动态，而没有利用其输入时提供的语义线索。为了减轻这个问题，我们引进CAB，装备有一个训练程序设计的运动预测模型来促进语义情景信息的使用。我们还引入了两种新指标 -- 分散和趋同到范围 -- 以衡量连续预测的时间一致性，我们在标准指标中发现缺少这些预测。我们的方法是根据广泛采用的nuScenes预测基准进行评估的。
原文题目：Raising context awareness in motion forecasting
原文：Learning-based trajectory prediction models have encountered great success, with the promise of leveraging contextual information in addition to motion history. Yet, we find that state-of-the-art forecasting methods tend to overly rely on the agent's dynamics, failing to exploit the semantic cues provided at its input. To alleviate this issue, we introduce CAB, a motion forecasting model equipped with a training procedure designed to promote the use of semantic contextual information. We also introduce two novel metrics -- dispersion and convergence-to-range -- to measure the temporal consistency of successive forecasts, which we found missing in standard metrics. Our method is evaluated on the widely adopted nuScenes Prediction benchmark.
提高运动预测中的情景意识.pdf