Yang Xiao

Yang Xiao

she/her

Senior software engineer, autonomous driving

Publications

You can also find my articles on my Google Scholar profile.

Books

The effect of weather on the performance of mm-wave and sub-THz automotive radar

Published in Advances in Weather Radar. Volume 3: Emerging applications, IET, 2024

Abstract- Full channel characterization is a vital part of research before launching new sensors for any applications. The requirement for higher image resolution close to the optical sensors while able to operate in all-weather and light conditions as well as being compact in size and light weight grows an interest for sub-THz automotive sensors. The basic step for channel characterization for outdoor application is described in this chapter. The channel characterization for sub-THz automotive radars is conducted alongside the current automotive radar frequency to provide meaningful understanding of benefit and drawbacks of increasing the frequency of operation to sub-THz region. The experimental and analytical studies in this chapter had been made with the main goal to demonstrate the feasibility of sub-THz sensing for outdoor application, in particular for automotive sensing. It has been shown that for all components of the propagation channel, the attenuation is within the acceptable range and there is no anticipated showstopper which would prevent development of sub-THz automotive sensors for future demands.

Recommended citation: Norouzian, F., Y. Xiao, and M. Gashinova. "The effect of weather on the performance of mm-wave and sub-THz automotive radar." (2024): 161-220.

Selected Problems of High-Resolution Automotive Imaging Radar

Published in University of Birmingham. Ph.D. thesis, 2023

Abstract- This thesis aims at two selected problems in the development of high-resolution au- tomotive imaging radar: 1) The feasibility of using sub-THz for the next generation of automotive radar; 2) The development of the physics-based image segmentation approach on the automotive radar imagery. The wide range of feasibility studies on the use of sub-THz frequencies for auto- motive radar have been undertaken in the Microwave Integrated Systems Laboratory (MISL) at the University of Birmingham, and the candidate is in charge of the included study on the theoretical modelling and experimental verification of the attenuation through the vehicle infrastructures which is the first part of this thesis. The importance of this work is related to the fact that automotive radar is placed within the car infras- tructure. Therefore, it would be a potential show-stopper in the development of this innovation if attenuation within the car bumper or badge is prohibitively high. Both theoretical modelling and experimental measurement are conducted by considering the impact factors on the propagation properties of the sub-THz signal such as the incident angle, frequency, characteristic parameters of materials, and the thicknesses of infrastructure layers. The transmissivity of multilayered structure has been modelled and good agreement with the results of measurements was demonstrated, so that the developed approach can be used in further studies on propagation through car infrastruc- ture. The published results on transmissivity and complex permittivity of automotive paints are valuable for researchers in either field of THz technology or automotive radar. The image segmentation on automotive radar maps aims at identifying the passable and impassable areas for path planning in autonomous driving. Contrary to traditional radar, radar clutter is regarded as the physical meaningful information, which can deliver valuable feature information for surface characterization, and enable the full scene reconstruction of automotive radar maps. The proposed novel segmentation algorithm is a hybrid method composed of pre-segmentation based on image processing methods, and the region classification using the multivariate Gaussian distribution (MGD) classifier developed based on the statistical distribution feature parameters of radar returns of various areas. Moving target indication (MTI) is implemented for the first time based on frame-to-frame context association. The end-to-end segmentation framework is therefore achieved robustly with good segmentation performance, and automatically without human intervention.

Recommended citation: Xiao, Yang. Selected Problems of High-Resolution Automotive Imaging Radar. Diss. University of Birmingham, 2023.
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Journal Articles

Image segmentation and region classification in automotive high-resolution radar imagery

Published in IEEE Sensors Journal, 2020

Abstract—Image segmentation and classification of sur-faces and obstacles in automotive radar imagery are the key technologies to provide valuable information for path planning in autonomous driving. As opposed to traditional radar processing, where clutter is considered as an unwanted return and should be effectively removed, autonomous driving requires full scene characterization. Hence, clutter carries necessary information for situational awareness of the autonomous platform and needs to be fully assessed to find the passable areas. In this paper, we proposed a method of automatic segmentation of automotive radar images based on two main steps: unsupervised image pre-segmentation using marker-based watershed transformation, followed by the supervised segmentation and classification of regions containing objects and surfaces based on the use of statistical distribution parameters. Several distributions were considered to characterize returns from specific region types of interest within the scene (denoted as classes) in calibrated radar imagery—the extracted distribution parameters were assessed for their ability to distinguish each class. These parameters were then used as features in a multivariate Gaussian distribution model classifier. Both the performances of the proposed supervised classification algorithm and the automatically segmented results were investigated using F1-score and Jaccard similarity coefficients, respectively.

Recommended citation: Xiao, Yang, Liam Daniel, and Marina Gashinova. "Image segmentation and region classification in automotive high-resolution radar imagery." IEEE Sensors Journal 21.5 (2020): 6698-6711.
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Modeling and experiment verification of transmissivity of low-THz radar signal through vehicle infrastructure

Published in IEEE Sensors Journal, 2020

Abstract—This paper is concerned with modelling of the transmissivity of Low-Terahertz waves through automotive bumper and headlight cover material. This work is part of wider comprehensive studies on the potential for the use higher frequency bands for future automotive sensors. Theo- retical models for transmissivity prediction are described, the methodology of experimentation is discussed and experimental results are presented. The theoretical models of reflection and transmission of different base materials which are cov- ered by different layers of paint are based on Fresnel theory, and the phenomena caused by the half wavelength thickness of the medium is analyzed mathematically. The experimental verification of the models in this paper have been undertaken at 300 GHz and 670 GHz, using 77 GHz as a reference frequency.

Recommended citation: Y. Xiao, F. Norouzian, E. G. Hoare, E. Marchetti, M. Gashinova and M. Cherniakov, "Modeling and Experiment Verification of Transmissivity of Low-THz Radar Signal Through Vehicle Infrastructure," in IEEE Sensors Journal, vol. 20, no. 15, pp. 8483-8496, 1 Aug.1, 2020, doi: 10.1109/JSEN.2020.2982984
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Conference Papers

Automotive Paint Permittivity Estimation in Low-THz frequency

Published in 2024 17th United Conference on Millemetre Waves and Terahertz Technologies (UCMMT), 2024

Abstract- The propagation characteristics of radar signal through vehicle infrastructure depends on electro-physical properties of a media, in particularly complex permittivity of automotive paint. In this paper, complex permittivities of various automotive paints are measured within the range from 0.14 THz to 1.1 THz using Terahertz Vector Network Analyzer (THz- VNA). The measured complex permittivities are then used to evaluate the attenuation and reflectivity of aggregate multi-layer paint.

Recommended citation: Y. Xiao, F. Norouzian, E. G. Hoare, A. Bystrov, M. Gashinova and M. Cherniakov, "Automotive Paint Permittivity Estimation in Low-THz frequency," 2024 17th United Conference on Millemetre Waves and Terahertz Technologies (UCMMT), Palermo, Italy, 2024, pp. 56-61, doi: 10.1109/UCMMT62975.2024.10737755.
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Universal Image Segmentation Framework on High-resolution Automotive Radar Map

Published in International Conference on Radar Systems (RADAR 2022), 2022

Abstract- A universal image segmentation framework, which can be applied to various high-resolution automotive radar imagery produced by different beamforming strategies, is expected in the radar community to provide robust support to the development of autonomous driving. This paper estimates the universality of the segmentation framework, which is developed based on radar data produced by the mechanical steer beamforming, by directly implementing it onto another high-resolution radar imagery produced by the beamforming strategy of MIMO Doppler beam sharpening (DBS). The comparison of the distribution features of two parts of data shows that the return power level shift caused by the resolution difference is the major factor that needs to be compensated for the framework transfer implementation. The details of the universal segmentation framework are given to show that this can significantly simplify the complicated manual labelling and feature extraction. The segmentation results are discussed with the analysis of the performance and the potential future work.

Recommended citation: Y. Xiao, S. Cassidy, L. Daniel, S. Pooni, M. Cherniakov and M. Gashinova, "Universal image segmentation framework on high-resolution automotive radar map," International Conference on Radar Systems (RADAR 2022), Hybrid Conference, Edinburgh, UK, 2022, pp. 226-231, doi: 10.1049/icp.2022.2320.
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Automotive Radar Image Segmentation with Frame Fusion

Published in International Conference on Radar Systems (RADAR 2022), 2022

Abstract- Image segmentation on automotive radar imagery is the key technique for identifying the passable and impassable regions for path planning in autonomous or assistive driving. The availability of consecutive frames which measure the driving scene shifted along with the timeline enables improved segmentation on radar imagery. The frame fusion on automotive radar map is implemented as a two-step procedure: 1) The pixel-to-pixel mapping between consecutive frames is achieved based on an inertial measurement unit (IMU); 2) The information fusion of consecutive frames is achieved based on the Kalman filter. The frame fusion operation leads to correct classification of the initially ‘unknown” regions and overall improves the confidence of classification compared to single frame segmentation. The segmentation results with frame fusion are presented and compared with the results of single frame segmentation to demonstrate the segmentation improvement.

Recommended citation: Y. Xiao et al., "Automotive radar image segmentation with frame fusion," International Conference on Radar Systems (RADAR 2022), Hybrid Conference, Edinburgh, UK, 2022, pp. 243-247, doi: 10.1049/icp.2022.2323.
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The End-to-End Segmentation on Automotive Radar Imagery

Published in 18th European Radar Conference (EuRAD 2021) held in London, UK., 2022

Abstract — Segmentation and classification of surfaces and objects in automotive radar imagery are key techniques to identify the passable region for path planning in autonomous driving. The end-to-end segmentation on automotive radar imagery is proposed in this paper, where the input B-scope automotive radar map is processed to output the segmented radar map with labeled area classes. The algorithm discussed in this paper is the extension of our previous published work [1], where we proposed two-stage segmentation processed including (i) pre-segmentation using watershed transformation (WT), and (ii) the region classification based on the Multivariate Gaussian Distribution (MGD) classifier and the extracted distribution features. In the current paper, we use the B-scope radar map representation to simplify the coordinate transformation procedure as compared to PPI image representation. Secondly to improve classification of low-return regions two-tier segmentation process is introduced, where after the first classification of regions of high return, the more subtle classification is made between classes of low returns. Radar test dataset, collected in outdoor driving scenarios and labeled according to optical ground truth is used for assessing the Jaccard similarity coefficient (JSC) performance of segmentation results, which show higher accuracy of classification than in our previous algorithm [1].

Recommended citation: Y. Xiao, L. Daniel and M. Gashinova, "The End-to-End Segmentation on Automotive Radar Imagery," 2021 18th European Radar Conference (EuRAD), London, United Kingdom, 2022, pp. 265-268, doi: 10.23919/EuRAD50154.2022.9784524.
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Feature-based classification for image segmentation in automotive radar based on statistical distribution analysis

Published in 2020 IEEE Radar Conference, 2020

Abstract—Segmentation and potential classification of surface and obstacle regions in automotive radar imagery is the key enabler of effective path planning in autonomous driving. As opposed to traditional radar processing where clutter is considered as an unwanted return and should be effectively removed, autonomous driving requires full scene assessment, where clutter carries necessary information for situational awareness of the autonomous platform and needs to be fully assessed to find the passable areas. In this paper, the statistical distribution features of the radar intensity data of several road-related scenes including asphalt, grass, shadow and target object areas are investigated. The algorithm of classification is developed based on distribution feature extraction and a multivariate Gaussian distribution (MGD) model. Under test dataset recorded by multi-sensor suit was used to evaluate the confusion matrix and F1 score of this classification algorithm.

Recommended citation: Y. Xiao, L. Daniel and M. Gashinova, "Feature-based Classification for Image Segmentation in Automotive Radar Based on Statistical Distribution Analysis," 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 2020, pp. 1-6, doi: 10.1109/RadarConf2043947.2020.9266596.
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Transmissivity Through Automotive Bumpers at mm-wave and Low-THz Frequencies

Published in The International Radar Symposium IRS 2019, June 26-28, 2019, Ulm, Germany, 2019

Abstract: This paper investigates signal attenuation through automotive bumpers at the conventional automotive radar frequency (77GHz) and at a Low-THz frequency (300 GHz). A Frequency-Modulated Continues-Wave (FMCW) radar operating at 77 GHz and a Stepped Frequency Radar (SFR) operating at 300 GHz are used in this experiment. The measured transmissivity through three bumper samples are compared at both frequencies to analyze the performance difference between the current automotive radar and prospective Low-THz radars. Transmissivity through the bumper samples show difference of around 1- 2 dB between the two frequencies under study.

Recommended citation: Y. Xiao et al., "Transmissivity Through Automotive Bumpers at mm-wave and Low-THz Frequencies," 2019 20th International Radar Symposium (IRS), Ulm, Germany, 2019, pp. 1-6, doi: 10.23919/IRS.2019.8768112.
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