Shengjie Xu

1156 High St #SOE3

Electrical and Computer Engineering

Santa Cruz, CA 95064

I am a second-year M.S. at UC Santa Cruz advised by Leilani H. Gilpin and James Davis. Prior to that, I received my B.S. from Hebei University of Technology. Before joining UC Santa Cruz, I was a senior researcher at CATARC working on computer vision and control in autonomous vehicle.

I’m generally interested in how we connect the visual and the physical world.

My CV can be found here. Also, my drawings.

Research Interests

Physical Scene Understanding
3D Vision, Computer Graphics
Explainable AI
Self-driving Cars

Education

		University of California, Santa Cruz
		Sep. 2021 - Mar. 2023
		M.S. in Electrical and Computer Engineering
		Hebei University of Technology
		Sep. 2007 - Jun. 2011
		B.S. in Automotive Engineering

News

Oct 22, 2022	Our abstract paper was presented as the only poster representing UCSC at BayLearn 2022
Oct 20, 2022	One paper was accepted at NeurIPS 2022 Workshop TEA
Oct 17, 2022	CASMI of Northwestern University has reported our Danger work, see our news

Selected publications

A Framework for Generating Dangerous Scenes for Testing Robustness

Xu, Shengjie, Mi, Lan, and Gilpin, Leilani H.

In NeurIPS 2022 Workshop - Progress and Challenges in Building Trustworthy Embodied AI 2022

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Benchmark datasets for autonomous driving, such as KITTI, nuScenes, Argoverse, or Waymo are realistic but designed to be faultless. These datasets do not contain errors, difficult driving maneuvers, or other corner cases. We propose a framework for perturbing autonomous vehicle datasets, the DANGER framework, which generates edge-case images on top of current autonomous driving datasets. The input to DANGER are photorealistic datasets from real driving scenarios. We present the DANGER algorithm for vehicle position manipulation and the interface towards the renderer module, and present five scenario-level dangerous primitives generation applied to the virtual KITTI and virtual KITTI 2 datasets. Our experiments prove that DANGER can be used as a framework for expanding the current datasets to cover generative while realistic and anomalous corner cases.
The Synthetic 3DOF Wheel Force for Passenger Vehicle Based on Predicted Frequency Response Function Model

Xu, Shengjie

In WCX World Congress Experience 2018

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To determine the vehicle chassis requirements, wheel force transducer (WFT) have been the best option when it is being used in targeting customer correlation or determining the effective use of the proving ground. However, using wheel force transducer in customer correlation fleet test is often unfeasible due to the huge cost and low practicability. As a result, engineers have to choose other transducer measures. This paper describes an effective approach of wheel force prediction by using the frequency response function (FRF) model of vehicle dynamic system. A vehicle system linear modelling technique is used. For the system identification of FRF, the acceleration and wheel force time history data, as system input and output, are collected from an instrumented passenger car as it traverses in different real-world proving ground surfaces. The obtained FRF represents the complex suspension mechanical model. Once the FRF is calculated, the predicted force signal can be implemented. The quality evaluation of prognostication results is made by comparing the synthetic and real-world wheel force signals in time/frequency domain and frequency-related pseudo damage. The result indicates that the FRF derived from real-world data is an effective modelling tool. Furthermore, it is a promising application in the field of customer usage fleet test and suspension dynamic system modelling or control.