CERES :: Browsing by Author "Lv, Chen"

Browsing by Author "Lv, Chen"

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Open Access
Advanced sensing and control for connected and automated vehicles
(MDPI, 2022-02-16) Huang, Chao; Du, Haiping; Zhao, Wanzhong; Zhao, Yifan; Yan, Fuwu; Lv, Chen
In recent years, connected and automated vehicles (CAV) have been a transformative technology that is expected to reduce emissions and change and improve the safety and efficiency of the mobilities [...]
Open Access
Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision
(IEEE, 2018-05-01) Xing, Yang; Lv, Chen; Chen, Long; Wang, Huaji; Wang, Hong; Cao, Dongpu; Velenis, Efstathios; Wang, Fei-Yue
Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.
Open Access
Analysis of autopilot disengagements occurring during autonomous vehicle testing
(IEEE, 2017-12-20) Lv, Chen; Cao, Dongpu; Zhao, Yifan; Auger, Daniel J.; Sullman, Mark; Wang, Huaji; Millen Dutka, Laura; Skrypchuk, Lee; Mouzakitis, Alexandros
In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle U+02BC s safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016, seven manufacturers submitted their first disengagement reports: Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers U+0028 SAE U+0029 Level 2 and Level 3 automation technologies. Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed.
Open Access
Characterization of driver neuromuscular dynamics for human-automation collaboration design of automated vehicles
(IEEE, 2018-03-05) Lv, Chen; Wang, Huaji; Cao, Dongpu; Zhao, Yifan; Auger, Daniel J.; Sullman, Mark; Matthias, Rebecca; Skrypchuk, Lee; Mouzakitis, Alexandros
In order to design an advanced human-automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper a dynamic model of drivers' neuromuscular interaction with a steering wheel is firstly established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver-steering-wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss-Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles.
Open Access
Cyber-physical system based optimization framework for intelligent powertrain control
(Society of Automotive Engineers, 2017-03-28) Lv, Chen; Wang, Hong; Zhao, Bolin; Cao, Dongpu; Huaji, Wang; Zhang, Junzhi; Li, Yutong; Yuan, Ye
The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented. Simulation-based parameter optimizations are carried out according to the objective functions. Simulation results show that an electric powertrain with intelligent controller can perform its tasks well under sport, eco, and normal driving modes. The vehicle further improves overall performance in vehicle dynamics, ride comfort, and energy efficiency. The results validate the feasibility and effectiveness of the proposed CPS-based optimization framework, and demonstrate its advantages over a baseline benchmark.
Open Access
Data for "An Orientation Sensor based Head Tracking System for Driver Behaviour Monitoring"
(Cranfield University, 2017-11-21 13:42) Zhao, Yifan; Görne, Lorenz; Yuen, Iek-Man; Cao, Dongpu; Sullman, Mark; Auger, Daniel; Lv, Chen; Wang, Huaji; Matthias, Rebecca; Skrypchuk, Lee; Mouzakitis, Alexandros
Data used for this paper - files created in MATLAB.
Open Access
Driver activity recognition for intelligent vehicles: a deep learning approach
(IEEE, 2019-04-01) Xing, Yang; Lv, Chen; Wang, Huaji; Cao, Dongpu; Velenis, Efstathios; Wang, Fei-Yue
Driver decisions and behaviors are essential factors that can affect the driving safety. To understand the driver behaviors, a driver activities recognition system is designed based on the deep convolutional neural networks (CNN) in this study. Specifically, seven common driving activities are identified, which are the normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle radio device, texting, and answering the mobile phone, respectively. Among these activities, the first four are regarded as normal driving tasks, while the rest three are classified into the distraction group. The experimental images are collected using a low-cost camera, and ten drivers are involved in the naturalistic data collection. The raw images are segmented using the Gaussian mixture model (GMM) to extract the driver body from the background before training the behavior recognition CNN model. To reduce the training cost, transfer learning method is applied to fine tune the pre-trained CNN models. Three different pre-trained CNN models, namely, AlexNet, GoogLeNet, and ResNet50 are adopted and evaluated. The detection results for the seven tasks achieved an average of 81.6% accuracy using the AlexNet, 78.6% and 74.9% accuracy using the GoogLeNet and ResNet50, respectively. Then, the CNN models are trained for the binary classification task and identify whether the driver is being distracted or not. The binary detection rate achieved 91.4% accuracy, which shows the advantages of using the proposed deep learning approach. Finally, the real-world application are analysed and discussed.
Open Access
Driver anomaly quantification for intelligent vehicles: a contrastive learning approach with representation clustering
(IEEE, 2022-03-30) Hu, Zhongxu; Xing, Yang; Gu, Weihao; Cao, Dongpu; Lv, Chen
Driver anomaly quantification is a fundamental capability to support human-centric driving systems of intelligent vehicles. Existing studies usually treat it as a classification task and obtain discrete levels for abnormalities. Meanwhile, the existing data-driven approaches depend on the quality of dataset and provide limited recognition capability for unknown activities. To overcome these challenges, this paper proposes a contrastive learning approach with the aim of building a model that can quantify driver anomalies with a continuous variable. In addition, a novel clustering supervised contrastive loss is proposed to optimize the distribution of the extracted representation vectors to improve the model performance. Compared with the typical contrastive loss, the proposed loss can better cluster normal representations while separating abnormal ones. The abnormality of driver activity can be quantified by calculating the distance to a set of representations of normal activities rather than being produced as the direct output of the model. The experiment results with datasets under different modes demonstrate that the proposed approach is more accurate and robust than existing ones in terms of recognition and quantification of unknown abnormal activities.
Open Access
Driver lane change intention inference for intelligent vehicles: framework, survey, and challenges
(IEEE, 2019-03-06) Xing, Yang; Lv, Chen; Wang, Huaji; Wang, Hong; Ai, Yunfeng; Cao, Dongpu; Velenis, Efstathios; Wang, Fei-Yue
Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.
Open Access
Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer
(Springer, 2024-01-11) Xing, Yang; Hu, Zhongxu; Mo, Xiaoyu; Hang, Peng; Li, Shujing; Liu, Yahui; Zhao, Yifan; Lv, Chen
Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles.
Open Access
Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine
(Elsevier, 2017-10-04) Xing, Yang; Lv, Chen; Cao, Dongpu; Wang, Huaji; Zhao, Yifan
Measuring driver workload is of great significance for improving the understanding of driver behaviours and supporting the improvement of advanced driver assistance systems technologies. In this paper, a novel hybrid method for measuring driver workload estimation for real-world driving data is proposed. Error reduction ratio causality, a new nonlinear causality detection approach, is being proposed in order to assess the correlation of each measured variable to the variation of workload. A full model describing the relationship between the workload and the selected important measurements is then trained via a support vector regression model. Real driving data of 10 participants, comprising 15 measured physiological and vehicle-state variables are used for the purpose of validation. Test results show that the developed error reduction ratio causality method can effectively identify the important variables that relate to the variation of driver workload, and the support vector regression based model can successfully and robustly estimate workload.
Open Access
Dual-envelop-oriented moving horizon path tracking control for fully automated vehicles
(Elsevier, 2017-02-14) Guo, Hongyan; Liu, Jun; Cao, Dongpu; Chen, Hong; Yu, Ru; Lv, Chen
A novel description of dual-envelop-oriented path tracking issue is presented for fully automated vehicles which considers shape of vehicle as inner-envelop (I-ENV) and feasible road region as outer-envelop (O-ENV). Then implicit linear model predictive control (MPC) approach is proposed to design moving horizon path tracking controller in order to solve the situations that may cause collision and run out of road in traditional path tracking method. The proposed MPC controller employed varied sample time and varied prediction horizon and could deal with modelling error effectively. In order to specify the effectiveness of the proposed dual-envelop-oriented moving horizon path tracking method, veDYNA-Simulink joint simulations in different running conditions are carried out. The results illustrate that the proposed path tracking scheme performs well in tracking the desired path, and could increase path tracking precision effectively.
Open Access
An ensemble deep learning approach for driver lane change intention inference
(Elsevier, 2020-04-23) Xing, Yang; Lv, Chen; Wang, Huaji; Cao, Dongpu; Velenis, Efstathios
With the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers’ intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts.
Open Access
Guest editorial: Decision making and control for connected and automated vehicles
(Institution of Engineering and Technology (IET), 2022-10-17) Lv, Chen; Hang, Peng; Xing, Yang; Nguyen, Anh-Tu; Jolfaei, Alireza
Open Access
Hazard-evaluation-oriented moving horizon parallel steering control for driver-automation collaboration during automated driving
(IEEE, 2018-08-14) Guo, Hongyan; Song, Linhuan; Liu, Jun; Wang, Fei-Yue; Cao, Dongpu; Chen, Hong; Lv, Chen; Luk, Patrick Chi-Kwong
Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon optimization. The framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation, the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable manner. The automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions.
Open Access
High-precision hydraulic pressure control based on linear pressure-drop modulation in valve critical equilibrium state
(IEEE, 2017-09-11) Lv, Chen; Wang, Hong; Cao, Dongpu
High precision and fast response are of great significance for hydraulic pressure control in automotive braking systems. In this paper, a novel sliding mode control based high-precision hydraulic pressure feedback modulation is proposed. Dynamical models of the hydraulic brake system including valve dynamics are established. An open loop load pressure control based on the linear relationship between the pressure-drop and coil current in valve critical open equilibrium state is proposed, and also experimentally validated on a hardware-in-the-loop test rig. The control characteristics under different input pressures and varied coil currents are investigated. Moreover, the sensitivity of the proposed modulation on valve's key structure parameters and environmental temperatures are explored with some unexpected drawbacks. In order to achieve better robustness and precision, a sliding mode control based closed loop scheme is developed for the linear pressure-drop modulation. Comparative tests between this method and the existing methods are carried out. The results validate the effectiveness and superior performance of the proposed closed loop modulation method.
Open Access
Human-machine collaboration for automated driving using an intelligent two-phase haptic interface
(Wiley, 2021-02-12) Lv, Chen; Li, Yutong; Xing, Yang; Huang, Chao; Cao, Dongpu; Zhao, Yifan; Liu, Yahui
Prior to realizing fully autonomous driving, human intervention is periodically required to guarantee vehicle safety. This poses a new challenge in human–machine interaction, particularly during the control authority transition from automated functionality to a human driver. Herein, this challenge is addressed by proposing an intelligent haptic interface based on a newly developed two‐phase human–machine interaction model. The intelligent haptic torque is applied to the steering wheel and switches its functionality between predictive guidance and haptic assistance according to the varying state and control ability of human drivers. This helps drivers gradually resume manual control during takeover. The developed approach is validated by conducting vehicle experiments with 26 participants. The results suggest that the proposed method effectively enhances the driving state recovery and control performance of human drivers during takeover compared with an existing approach. Thus, this new method further improves the safety and smoothness of human–machine interaction in automated vehicles.
Open Access
Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle
(IEEE, 2018-02-21) Lv, Chen; Xing, Yang; Lu, Chao; Liu, Yahui; Guo, Hongyan; Gao, Hongbo; Cao, Dongpu
The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios.
Open Access
Hybrid-learning-based driver steering intention prediction using neuromuscular dynamics
(IEEE, 2021-02-23) Xing, Yang; Lv, Chen; Liu, Ya-hui; Zhao, Yifan; Cao, Dongpu; Kawahara, Sadahiro
The emerging automated driving technology poses a new challenge on driver-automation collaboration. In this study, oriented by human-machine mutual understanding, a driver steering intention prediction method is proposed to better understand human driver's expectation during driver-vehicle interaction. The steering intention is predicted based on a novel hybrid-learning-based time-series model with deep learning networks. Two different driving modes, namely, both hands and single right-hand driving modes, are studied. Different electromyography (EMG) signals from the upper limb muscles are collected and used for the steering intention prediction. The relationship between the neuromuscular dynamics and the steering torque is analyzed first. Then, the hybrid-learning-based model is developed to predict both the continuous and discrete steering intentions. The two intention prediction networks share the same temporal pattern exaction layer, which is built with the Bi-directional Recurrent Neural Network (RNN) and Long short-term memory (LSTM) cells. The model prediction performance is evaluated with a varied history and prediction horizon to exploit the model capability further. The experimental data are collected from 21 participants of varied ages and driving experience. The results show that the proposed method can achieve a prediction accuracy of around 95% steering under the two driving modes.
Open Access
Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition
(IEEE, 2018-12-25) Xing, Yang; Lv, Chen; Zhang, Zhaozhong; Wang, Huaji; Na, Xiaoxiang; Cao, Dongpu; Velenis, Efstathios; Wang, Fei-Yue
Driver decisions and behaviors regarding the surrounding traffic are critical to traffic safety. It is important for an intelligent vehicle to understand driver behavior and assist in driving tasks according to their status. In this paper, the consumer range camera Kinect is used to monitor drivers and identify driving tasks in a real vehicle. Specifically, seven common tasks performed by multiple drivers during driving are identified in this paper. The tasks include normal driving, left-, right-, and rear-mirror checking, mobile phone answering, texting using a mobile phone with one or both hands, and the setup of in-vehicle video devices. The first four tasks are considered safe driving tasks, while the other three tasks are regarded as dangerous and distracting tasks. The driver behavior signals collected from the Kinect consist of a color and depth image of the driver inside the vehicle cabin. In addition, 3-D head rotation angles and the upper body (hand and arm at both sides) joint positions are recorded. Then, the importance of these features for behavior recognition is evaluated using random forests and maximal information coefficient methods. Next, a feedforward neural network (FFNN) is used to identify the seven tasks. Finally, the model performance for task recognition is evaluated with different features (body only, head only, and combined). The final detection result for the seven driving tasks among five participants achieved an average of greater than 80% accuracy, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behavior recognition.

Browsing by Author "Lv, Chen"

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