Abstract
Planning methods for parking are an important topic in the realm of autonomous driving. To achieve successful parking, complicated maneuvers are required to be reasonably performed in a very limited space. Moreover, since unstructured parking scenarios are lack of significant common features, creating useful heuristics manually to adapt to changing conditions is a non-trivial task. Therefore, we propose a two-stage scheme, Deep Neural Networks based path prediction for the first stage and sampling-based optimization for the second stage. Specifically, a customized network is used for predicting a feasible path with a high successful rate and the prediction error is modeled by Gaussian in the second stage. With the modeled error, a variant Bi-RRT\(^*\) method is specially-designed to correct the possible prediction error and further improve the path quality. We carry out experiments to validate the performance of the proposed scheme and compare it to existing methods. Experimental results demonstrated that our planning scheme can infer a path by neural networks within 25 ms and plan a final path around 75 ms.
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Acknowledgement
This research is supported partly by GuangDong Basic and Applied Basic Research Foundation (2020A1515110199), partly by Guangdong HUST Industrial Technology Research Institute Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization (2020B1212060014) and partly by Shenzhen science and technology program (JCYJ20210324122203009, JCYJ20180508152434975).
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Shan, Y., Zhong, Z., Fan, L., Kai, H. (2023). DeepParking: Deep Learning-Based Planning Method for Autonomous Parking. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_2
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DOI: https://doi.org/10.1007/978-3-031-32910-4_2
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