Automated and Autonomous Driving in Freight Transport - Opportunities and Limitations

Daduna, Joachim R.

doi:10.1007/978-3-030-59747-4_30

Cited by 4 publications

(2 citation statements)

References 124 publications

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“…With the development of artificial intelligence, technologies such as autopilot and robot automatic cruise are gradually applied in various industries (Yoon et al, 2022;Daduna, 2020). The autopilot of intelligent mechanical equipment in the greenhouse, open-air farm and other environments is one of the important development directions of intelligent agriculture.…”

Section: Introductionmentioning

confidence: 99%

Research on Semantic Segmentation of Greenhouse Road Image

YANG,

WANG,

XIE

et al. 2023

INMATEH

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In order to realize the automatic driving of agricultural machinery in greenhouse, this paper uses image acquisition equipment to collect road images in greenhouse and makes data sets, builds SETR model based on Transformer framework and DeepLabv3+ model based on convolution neural network for semantic segmentation of road images in greenhouse, and verifies the semantic segmentation ability of the two models to road images in greenhouse. Several groups of training periods are set as observation points to observe the semantic segmentation effect of the two models on the greenhouse road image, and the test set which has not been trained by the model is used as the prediction object to verify the performance of the two models on the semantic segmentation of greenhouse road image. The SETR model reached 94.64% PA on the greenhouse road data set, and 82.72% mIoU, DeepLabv3+ model reached 90.80% PA and 72.35% mIoU on the greenhouse road data set. Both models have excellent performance in semantic segmentation of greenhouse road images, and the performance of SETR model is slightly better than that of DeepLabv3+ model. The semantic segmentation performance of the two models for greenhouse road images can meet the needs of actual deployment.

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Section: Introductionmentioning

confidence: 99%

Research on Semantic Segmentation of Greenhouse Road Image

YANG,

WANG,

XIE

et al. 2023

INMATEH

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“…Autonomous driving systems enables vehicles to automatically navigate open roads, performing specific tasks such as robotaxi transport [1] and unmanned shipping container transport [2]. This area of research has gained significant popularity [3,4].…”

Section: Introductionmentioning

confidence: 99%

Bird's Eye View Semantic Segmentation based on Improved Transformer for Automatic Annotation

2023

KSII TIIS

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High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.

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