Raising context awareness in motion forecasting

• URL: http://arxiv.org/abs/2109.08048v1
• Date: Thu, 16 Sep 2021 15:25:27 GMT
• Title: Raising context awareness in motion forecasting
• Authors: H\'edi Ben-Younes, \'Eloi Zablocki, Micka\"el Chen, Patrick P\'erez, Matthieu Cord
• Abstract summary: 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. Our method is evaluated on the widely adopted nuScenes Prediction benchmark.
• Score: 30.86735692571529
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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