TransParking: A Dual-Decoder Transformer Framework with Soft Localization for End-to-End Automatic Parking
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908642317434880 |
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| author | Du, Hangyu Chew, Chee-Meng |
| author_facet | Du, Hangyu Chew, Chee-Meng |
| contents | In recent years, fully differentiable end-to-end autonomous driving systems have become a research hotspot in the field of intelligent transportation. Among various research directions, automatic parking is particularly critical as it aims to enable precise vehicle parking in complex environments. In this paper, we present a purely vision-based transformer model for end-to-end automatic parking, trained using expert trajectories. Given camera-captured data as input, the proposed model directly outputs future trajectory coordinates. Experimental results demonstrate that the various errors of our model have decreased by approximately 50% in comparison with the current state-of-the-art end-to-end trajectory prediction algorithm of the same type. Our approach thus provides an effective solution for fully differentiable automatic parking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06071 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TransParking: A Dual-Decoder Transformer Framework with Soft Localization for End-to-End Automatic Parking Du, Hangyu Chew, Chee-Meng Computer Vision and Pattern Recognition In recent years, fully differentiable end-to-end autonomous driving systems have become a research hotspot in the field of intelligent transportation. Among various research directions, automatic parking is particularly critical as it aims to enable precise vehicle parking in complex environments. In this paper, we present a purely vision-based transformer model for end-to-end automatic parking, trained using expert trajectories. Given camera-captured data as input, the proposed model directly outputs future trajectory coordinates. Experimental results demonstrate that the various errors of our model have decreased by approximately 50% in comparison with the current state-of-the-art end-to-end trajectory prediction algorithm of the same type. Our approach thus provides an effective solution for fully differentiable automatic parking. |
| title | TransParking: A Dual-Decoder Transformer Framework with Soft Localization for End-to-End Automatic Parking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.06071 |