基于學習的軌道預測模型已經取得了巨大的成功，除了運動曆史外，還可以利用情境資訊。然而，我們發現，最先進的預測方法往往過度依賴于代理的動态，而沒有利用其輸入時提供的語義線索。為了減輕這個問題，我們引進CAB，裝備有一個訓練程式設計的運動預測模型來促進語義情景資訊的使用。我們還引入了兩種新名額 -- 分散和趨同到範圍 -- 以衡量連續預測的時間一緻性，我們在标準名額中發現缺少這些預測。我們的方法是根據廣泛采用的nuScenes預測基準進行評估的。
原文題目：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.
提高運動預測中的情景意識.pdf