TransParking: A Dual-Decoder Transformer Framework with Soft Localization for End-to-End Automatic Parking

• URL: http://arxiv.org/abs/2503.06071v1
• Date: Sat, 08 Mar 2025 05:41:24 GMT
• Title: TransParking: A Dual-Decoder Transformer Framework with Soft Localization for End-to-End Automatic Parking
• Authors: Hangyu Du, Chee-Meng Chew,
• Abstract summary: We present a vision-based transformer model for end-to-end automatic parking, trained using expert trajectories.<n> Experimental results demonstrate that the various errors of our model have decreased by approximately 50%.
• Score: 2.209921757303168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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