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Browsing by Author "Lv, Chen"

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An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

Liu, Yahui; Fan, Xiaoqian; Lv, Chen; Wu, Jian; Li, Liang; Ding, Dawei (Elsevier, 2017-09-04)

Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive ...
An integrated framework for multi-state driver monitoring using heterogeneous loss and attention-based feature decoupling

Hu, Zhongxu; Zhang, Yiran; Xing, Yang; Li, Qinghua; Lv, Chen (MDPI, 2022-09-29)

Multi-state driver monitoring is a key technique in building human-centric intelligent driving systems. This paper presents an integrated visual-based multi-state driver monitoring framework that incorporates head rotation, ...
Learning from the Dark Side: a parallel time series modelling framework for forecasting and fault detection on intelligent vehicles

Xing, Yang; Hu, Zhongxu; Hang, Peng; Lv, Chen (IEEE, 2023-12-13)

Time series vehicle state modelling is crucial in various real-world applications, such as fault detection, fault tolerance, optimization, and cyber security for intelligent vehicles (IVs). In this study, we propose a novel ...
Learning spatio-temporal representations with a dual-stream 3-D residual network for nondriving activity recognition

Yang, Lichao; Shan, Xiaocai; Lv, Chen; Brighton, James; Zhao, Yifan (IEEE, 2021-07-28)

Accurate recognition of non-driving activity (NDA) is important for the design of intelligent Human Machine Interface to achieve a smooth and safe control transition in the conditionally automated driving vehicle. However, ...
Levenberg-Marquardt backpropagation training of multilayer neural networks for state estimation of a safety critical cyber-physical system

Lv, Chen; Xing, Yang; Zhang, Junzhi; Na, Xiaoxiang; Li, Yutong; Liu, Teng; Cao, Dongpu; Wang, Fei-Yue (IEEE, 2017-11-24)

As an important safety critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive ...
Map-adaptive multimodal trajectory prediction using hierarchical graph neural networks

Mo, Xiaoyu; Xing, Yang; Liu, Haochen; Lv, Chen (IEEE, 2023-04-26)

Predicting the multimodal future motions of neighboring agents is essential for an autonomous vehicle to navigate complex scenarios. It is challenging as the motion of an agent is affected by the complex interaction among ...
Milestones in autonomous driving and intelligent vehicles - Part 1: control, computing system design, communication, HD map, testing, and human behaviors

Chen, Long; Li, Yuchen; Huang, Chao; Xing, Yang; Tian, Daxin; Li, Li; Hu, Zhongxu; Teng, Siyu; Lv, Chen; Wang, Jinjun; Cao, Dongpu; Zheng, Nanning; Wang, Fei-Yue (IEEE, 2023-05-29)

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this ...
Milestones in autonomous driving and intelligent vehicles: survey of surveys

Chen, Long; Li, Yuchen; Huang, Chao; Li, Bai; Xing, Yang; Tian, Daxin; Li, Li; Hu, Zhongxu; Na, Xiaoxiang; Li, Zixuan; Teng, Siyu; Lv, Chen; Wang, Jinjun; Cao, Dongpu; Zheng, Nanning; Wang, Fei-Yue (IEEE, 2022-11-24)

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this ...
Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network

Mo, Xiaoyu; Huang, Zhiyu; Xing, Yang; Lv, Chen (IEEE, 2022-02-01)

Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations. Two main challenges for ...
An orientation sensor based head tracking system for driver behaviour monitoring

Zhao, Yifan; Görne, Lorenz; Yuen, Iek-Man; Cao, Dongpu; Sullman, Mark; Auger, Daniel J.; Lv, Chen; Wang, Huaji; Matthias, Rebecca; Skrypchuk, Lee; Mouzakitis, Alexandros (MDPI, 2017-11-22)

Although at present legislation does not allow drivers in a Level 3 autonomous vehicle to engage in a secondary task, there may become a time when it does. Monitoring the behaviour of drivers engaging in various non-driving ...
Pattern recognition and characterization of upper limb neuromuscular dynamics during driver-vehicle interactions

Xing, Yang; Lv, Chen; Zhao, Yifan; Liu, Yahui; Cao, Dongpu; Kawahara, Sadahiro (Elsevier, 2020-09-07)

In this work, pattern recognition and characterization of the neuromuscular dynamics of driver upper limb during naturalistic driving were studied. During the human-in-the-loop experiments, two steering tasks, namely, the ...
Recognizing driver braking intention with vehicle data using unsupervised learning methods

Xing, Yang; Lv, Chen; Huaji, Wang; Wang, Hong; Cao, Dongpu (SAE International, 2017-03)

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system ...
Retrieving common discretionary lane changing characteristics from trajectories

Li, Li; Lv, Chen; Cao, Dongpu; Zhang, Jiajie (IEEE, 2017-11-09)

Conventional lane change methods directly collected steering angle data via onboard sensors to accurately capture the actions of individual drivers. We can hardly use such methods to collect massive data from examinees, ...
Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles

Hu, Zhongxu; Lou, Shanhe; Xing, Yang; Wang, Xiao; Cao, Dongpu; Lv, Chen (IEEE, 2022-08-01)

Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design ...
Simultaneous observation of hybrid states for cyber-physical systems: a case study of electric vehicle powertrain

Lv, Chen; Liu, Yahui; Hu, Xiaosong; Guo, Hongyan; Cao, Dongpu; Wang, Fei-Yue (IEEE, 2017-08-22)

As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the ...
A study on objective evaluation of vehicle steering comfort based on driver's electromyogram and movement trajectory

Liu, Yahui; Liu, Qi; Lv, Chen; Zheng, Minghui; Ji, Xuewu (IEEE, 2017-10-05)

The evaluation of driver's steering comfort, which is mainly concerned with the haptic driver–vehicle interaction, is important for the optimization of advanced driver assistance systems. The current approaches to investigating ...
Toward human-centered automated driving: a novel spatial-temporal vision transformer-enabled head tracker

Hu, Zhongxu; Zhang, Yiran; Xing, Yang; Zhao, Yifan; Cao, Dongpu; Lv, Chen (IEEE, 2022-01-26)
A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Ji, Xuewu; He, Xiangkun; Lv, Chen; Liu, Yahui; Wu, Jian (Taylor & Francis, 2017-11-20)

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) ...

Browsing by Author "Lv, Chen"

Browsing by Author "Lv, Chen"

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