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Yingfeng Cai on Muck Rack

Yingfeng Cai

As seen in: Nature, Wiley Online Library, arXiv, MDPI, Taylor & Francis Online, Journal of Advanced Transportation
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Articles

EIDrive: an efficient and interpretable visual-language framework for low-level control signal prediction in autonomous driving

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By Wei Dong, Yingfeng Cai, Xiaoqiang Sun, Ze Xi Liu, Hai Wang, Long Chen
| Nature Verified
Abstract In recent years, end-to-end autonomous driving models have shown promising potential by directly generating waypoints or control signals from sensor data. In particular, low-level control signals, such as steering angle and vehicle speed, directly determine vehicle motion, making their prediction accuracy and interpretability important to the safety and reliability of autonomous driving systems.
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Modeling vehicle dynamics with physics-informed deep operator network

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By Chenkai Tan, Yingfeng Cai, Hai Wang, Long Chen
| Taylor & Francis Online
In this paper, we present a novel data-driven approach for modelling vehicle dynamics that integrates the physics-informed deep operator network with the principles of linear time-invariant (LTI) state space modelling. This approach aims to achieve precise and robust data-driven vehicle dynamics by utilising vehicle physical models. These physical models have been validated experimentally and through rigorous theoretical derivations.
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Highway Safety with an Intelligent Headlight System for Improved Nighttime Driving

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By Yingfeng Cai, Ammar Jafaripournimchahi, Hai Wang, Jacob Kwaku Nkrumah
| MDPI
1. Introduction Headlights are essential components on vehicles whose functions become more pronounced during nighttime and adverse weather conditions while driving. The headlight is equipped with two main light beams, namely the low beam and the high beam. The low beam is employed to illuminate close and mid-range distances in front of the vehicle, offering the driver clearer visibility at short and medium ranges.
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