Vectorized Representation Dreamer (VRD): Dreaming-Assisted Multi-Agent Motion-Forecasting
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917699768025088 |
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| author | Schofield, Hunter Mirkhani, Hamidreza Elmahgiubi, Mohammed Rezaee, Kasra Shan, Jinjun |
| author_facet | Schofield, Hunter Mirkhani, Hamidreza Elmahgiubi, Mohammed Rezaee, Kasra Shan, Jinjun |
| contents | For an autonomous vehicle to plan a path in its environment, it must be able to accurately forecast the trajectory of all dynamic objects in its proximity. While many traditional methods encode observations in the scene to solve this problem, there are few approaches that consider the effect of the ego vehicle's behavior on the future state of the world. In this paper, we introduce VRD, a vectorized world model-inspired approach to the multi-agent motion forecasting problem. Our method combines a traditional open-loop training regime with a novel dreamed closed-loop training pipeline that leverages a kinematic reconstruction task to imagine the trajectory of all agents, conditioned on the action of the ego vehicle. Quantitative and qualitative experiments are conducted on the Argoverse 2 multi-world forecasting evaluation dataset and the intersection drone (inD) dataset to demonstrate the performance of our proposed model. Our model achieves state-of-the-art performance on the single prediction miss rate metric on the Argoverse 2 dataset and performs on par with the leading models for the single prediction displacement metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14415 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Vectorized Representation Dreamer (VRD): Dreaming-Assisted Multi-Agent Motion-Forecasting Schofield, Hunter Mirkhani, Hamidreza Elmahgiubi, Mohammed Rezaee, Kasra Shan, Jinjun Robotics Machine Learning For an autonomous vehicle to plan a path in its environment, it must be able to accurately forecast the trajectory of all dynamic objects in its proximity. While many traditional methods encode observations in the scene to solve this problem, there are few approaches that consider the effect of the ego vehicle's behavior on the future state of the world. In this paper, we introduce VRD, a vectorized world model-inspired approach to the multi-agent motion forecasting problem. Our method combines a traditional open-loop training regime with a novel dreamed closed-loop training pipeline that leverages a kinematic reconstruction task to imagine the trajectory of all agents, conditioned on the action of the ego vehicle. Quantitative and qualitative experiments are conducted on the Argoverse 2 multi-world forecasting evaluation dataset and the intersection drone (inD) dataset to demonstrate the performance of our proposed model. Our model achieves state-of-the-art performance on the single prediction miss rate metric on the Argoverse 2 dataset and performs on par with the leading models for the single prediction displacement metrics. |
| title | Vectorized Representation Dreamer (VRD): Dreaming-Assisted Multi-Agent Motion-Forecasting |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2406.14415 |