Motion Perceiver: Real-Time Occupancy Forecasting for Embedded Systems
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866911769368199168 |
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| author | Ferenczi, Bryce Burke, Michael Drummond, Tom |
| author_facet | Ferenczi, Bryce Burke, Michael Drummond, Tom |
| contents | This work introduces a novel and adaptable architecture designed for real-time occupancy forecasting that outperforms existing state-of-the-art models on the Waymo Open Motion Dataset in Soft IOU. The proposed model uses recursive latent state estimation with learned transformer-based functions to effectively update and evolve the state. This enables highly efficient real-time inference on embedded systems, as profiled on an Nvidia Xavier AGX. Our model, MotionPerceiver, achieves this by encoding a scene into a latent state that evolves in time through self-attention mechanisms. Additionally, it incorporates relevant scene observations, such as traffic signals, road topology and agent detections, through cross-attention mechanisms. This forms an efficient data-streaming architecture, that contrasts with the expensive, fixed-sequence input common in existing models. The architecture also offers the distinct advantage of generating occupancy predictions through localized querying based on a point-of-interest, as opposed to generating fixed-size occupancy images that render potentially irrelevant regions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_08879 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Motion Perceiver: Real-Time Occupancy Forecasting for Embedded Systems Ferenczi, Bryce Burke, Michael Drummond, Tom Robotics I.2.9; I.2.10 This work introduces a novel and adaptable architecture designed for real-time occupancy forecasting that outperforms existing state-of-the-art models on the Waymo Open Motion Dataset in Soft IOU. The proposed model uses recursive latent state estimation with learned transformer-based functions to effectively update and evolve the state. This enables highly efficient real-time inference on embedded systems, as profiled on an Nvidia Xavier AGX. Our model, MotionPerceiver, achieves this by encoding a scene into a latent state that evolves in time through self-attention mechanisms. Additionally, it incorporates relevant scene observations, such as traffic signals, road topology and agent detections, through cross-attention mechanisms. This forms an efficient data-streaming architecture, that contrasts with the expensive, fixed-sequence input common in existing models. The architecture also offers the distinct advantage of generating occupancy predictions through localized querying based on a point-of-interest, as opposed to generating fixed-size occupancy images that render potentially irrelevant regions. |
| title | Motion Perceiver: Real-Time Occupancy Forecasting for Embedded Systems |
| topic | Robotics I.2.9; I.2.10 |
| url | https://arxiv.org/abs/2306.08879 |