A Road Environment Prediction System for Intelligent Vehicle

Ma, Chao; Sun, Zhao; Pei, Shanshan; Liu, Chao; Cui, Feng

doi:10.1155/2021/5569295

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Performing epipolar rectification on the absolute phase. The disparity of the absolute phase between the left and right images is computed based on the principles of binocular imaging [25]:…”

Section: A Methods Of Dataset Constructingmentioning

confidence: 99%

A Robust End-to-End Speckle Stereo Matching Network for Industrial Scenes

Liu,

Yang,

et al. 2024

IEEE Access

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The detection capability of deep learning-based stereo matching in industrial applications is inherently limited due to challenges posed by weak texture and inconsistent reflectance, making it difficult to accurately recover complex surface details. To achieve accurate measurements, this paper presents an endto-end speckle stereo matching network that incorporates fringe, Gray code, and speckle projection patterns. The model is trained using a high-precision dataset consisting of thousands of pairs generated through binocular Gray code-assisted phase shifting. After establishing local correspondences between the left and right images using speckle patterns, the images are used as inputs to the network. The proposed network consists of two siamese 2D feature extraction networks. One network is dedicated to cost volume computation, while the other focuses on weight refinement feature extraction. The former network incorporates a lightweight module for extracting high-dimensional fusion features. These features are obtained from different dilation scales and randomly concatenated along the channel dimension. Patch convolution is utilized to effectively adapt to pixel features at various levels, reducing redundancy within the cost volume and improving the network's capacity to learn from ill-posed regions. Experimental results demonstrate that the proposed network achieves a significant improvement of approximately 10.7% in matching accuracy compared to state-of-the-art networks on public datasets. Furthermore, this method exhibits outstanding matching results when applied to diverse industrial scenarios. The reconstruction error for the radius of optical standard spheres is below 0.06mm, which meets the demands of the majority of industrial applications.INDEX TERMS Stereo matching, Gray code-assisted phase shifting, high robustness industrial imaging, group-wise volume & correction map

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Section: A Methods Of Dataset Constructingmentioning

confidence: 99%

A Robust End-to-End Speckle Stereo Matching Network for Industrial Scenes

Liu,

Yang,

et al. 2024

IEEE Access

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“…Vision sensors are the perception core of the autonomous driving system and are responsible for observing the environment around the vehicle. The vision sensor obtains the image data of the vehicle's front scenery (Ma et al, 2021) and calculates the three-dimensional point cloud (F. information through the traditional matching algorithm (Long, Xie, Mita, Ishimaru, & Shirai., 2014), which is the process of the perception module. The strategic module is completed by the controller that detects obstacles based on the three-dimensional point cloud information, obtains the position information and category information of the obstacles, adds a tracking module to the detection results of the obstacles, acquires the stable spatial position, speed, and acceleration of the obstacles, and finally calculates the time-to-collision (TTC) value of the target obstacle.…”

Section: User Big Data Managementmentioning

confidence: 99%

An Evaluation System Based on User Big Data Management and Artificial Intelligence for Automatic Vehicles

Pei

Zhu

2022

Journal of Organizational and End User Computing

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As artificial intelligence technique is widely used in the automatic driving system, the safety evaluation of automatic vehicles is considered to be the most important demand. Under this context, in this paper, an evaluation system, which is composed of several important evaluation projects is proposed based on big data. These indicators reflect the performance of the automatic driving system. Besides, the principle of the evaluation index and the data management scheme are explained. In terms of the evaluation projects, the online test and the offline test are included, when the former focuses on the function design that is as expected, while the latter aims to ensure the actual driving experience of the automatic driving system. The evaluated results provide optimization direction of the algorithm index. Furthermore, based on AI technology and user big data management, the system saves lots of test cost and guarantees algorithm performance and system stability.

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