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Towards High Accuracy Parking Slot Detection for Automated Valet Parking System

Tongji University-Qinghua Yang, Hui Chen, Junzhe Su, Jie Li
Published 2019-11-04 by SAE International in United States
Highly accurate parking slot detection methods are crucial for Automated Valet Parking (AVP) systems, to meet their demanding safety and functional requirements. While previous efforts have mostly focused on the algorithms’ capabilities to detect different types of slots under varying conditions, i.e. the detection rate, their accuracy has received little attention at this time. This paper highlights the importance of trustworthy slot detection methods, which address both the detection rate and the detection accuracy. To achieve this goal, an accurate slot detection method and a reliable ground-truth slot measurement method have been proposed in this paper. First, based on a 2D laser range finder, datapoints of obstacle vehicles on both sides of a slot have been collected and preprocessed. Second, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm has been improved to efficiently cluster these unevenly-distributed datapoints. After that, the Random Sample Consensus (RANSAC) algorithm has been improved to accurately fit the vehicles’ longitudinal contours. Finally, the candidate slot has been constructed and checked for its rationality. The final slot detection results have…
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Quantification of Meta-model and Parameter Uncertainties in Robust Design

Changan Automobile Engineering Institute-Jie Li, Yazhou Jiang, Helen Yu
Chongqing University-Chong Chen, Zhenfei Zhan
Published 2016-04-05 by SAE International in United States
To reduce the computational time of the iterations in robust design, meta-models are frequently utilized to approximate time-consuming computer aided engineering models. However, the bias of meta-model uncertainty largely affects the robustness of the prediction results, this uncertainty need to be addressed before design optimization. In this paper, an efficient uncertainty quantification method considering both model and parameter uncertainties is proposed. Firstly, the uncertainty of parameters are characterized by statistical distributions. The Bayesian inference is then performed to improve the predictive capabilities of the surrogate models, meanwhile, the model uncertainty can also be quantified in the form of variance. Monte Carlo sampling is finally utilized to quantify the compound uncertainties of model and parameter. Furthermore, the proposed uncertainty quantification method is used for robust design. A numerical example and a real-world vehicle lightweight case study are used to demonstrate the validity of the proposed method.
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Test Process and Correction of Automotive Wind Tunnel in Jilin University China

ASCL,Jilin University-Yingchao Zhang, Zhe Zhang, Jie Li, Shuxin Shao, Sai Guo
Published 2013-04-08 by SAE International in United States
Similar to the traditional method of aeronautical wind tunnel testing a procedure to correct for support interference and wind tunnel interference in the automotive wind tunnel of Jilin University is described.Due to the fact that a moving belt is installed in the centre region of the test section the model was kept in place by an external support system. However, for our tests the moving belt was stationary. In order to separate the different interference effects two tests were carried out with the car model connected and disconnected from the support-structure linked to a six-component underfloor balance. In this way the aerodynamic force on a car model, which is then still contaminated by wind tunnel interference effects and the secondary interference effects of the model support on to the car model can be determined. The latter effect is further determined with aid of computational fluid dynamics by comparing the results of the vehicle in an equivalent numerical stream with the support structure present but disconnected from the chassis of the car and with the support…
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