Browsing by Author "Cao, Dongpu"
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Item 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-YueLane 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.Item 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, AlexandrosIn 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.Item 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, AlexandrosIn 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.Item Open Access CogEmoNet: A cognitive-feature-augmented driver emotion recognition model for smart cockpit(IEEE, 2021-11-30) Li, Wenbo; Zeng, Guanzhong; Zhang, Juncheng; Xu, Yan; Xing, Yang; Zhou, Rui; Guo, Gang; Shen, Yu; Cao, Dongpu; Wang, Fei-YueDriver's emotion recognition is vital to improving driving safety, comfort, and acceptance of intelligent vehicles. This article presents a cognitive-feature-augmented driver emotion detection method that is based on emotional cognitive process theory and deep networks. Different from the traditional methods, both the driver's facial expression and cognitive process characteristics (age, gender, and driving age) were used as the inputs of the proposed model. Convolutional techniques were adopted to construct the model for driver's emotion detection simultaneously considering the driver's facial expression and cognitive process characteristics. A driver's emotion data collection was carried out to validate the performance of the proposed method. The collected dataset consists of 40 drivers' frontal facial videos, their cognitive process characteristics, and self-reported assessments of driver emotions. Another two deep networks were also used to compare recognition performance. The results prove that the proposed method can achieve well detection results for different databases on the discrete emotion model and dimensional emotion model, respectively.Item 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, YeThe 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.Item 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, AlexandrosData used for this paper - files created in MATLAB.Item Open Access Data for the paper "Analysis of Autopilot Disengagements Occurring during Autonomous Vehicle Testing"(Cranfield University, 2017-12-11 08:19) Lyu, Chen; Cao, Dongpu; Zhao, Yifan; Auger, Daniel; Sullman, Mark; Wang, HuajiData used in the paper "Analysis of Autopilot Disengagements Occurring during Autonomous Vehicle Testing".Item 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-YueDriver 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.Item 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, ChenDriver 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.Item Open Access Driver distraction detection using machine learning algorithms – an experimental approach(Inderscience, 2021-05-08) Zhang, Zhaozhong; Velenis, Efstathios; Fotouhi, Abbas; Auger, Daniel J.; Cao, DongpuDriver distraction is the leading cause of accidents that contributes to 25% of all road crashes. In order to reduce the risks posed by distraction, warning must be given to the driver once distraction is detected. According to the literature, no rankings of relevant features have been presented. In this study, the most relevant features in detecting driver distraction are identified in a closed testing environment. The relevant features are found to be the mean values of speed and lane deviation, maximum values of eye gaze in direction, and head movement in direction. After the relevant features have been identified, pre-processed data with relevant features are fed into decision tree classifiers to discriminate the data into normal and distracted driving. The results show that detection accuracy of 78.4% using decision tree can be achieved. By eliminating unhelpful features, the time required to process data is reduced by around 40% to make the proposed technique suitable for real-time application.Item 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-YueIntelligent 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.Item 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, YifanMeasuring 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.Item Open Access Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey(IEEE, 2017-07-04) Marina Martinez, Clara; Heucke, Mira; Wang, Fei-Yue; Gao, Bo; Cao, DongpuDriver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts' predictions of the future development.Item Open Access Effect of handling characteristics on minimum time cornering with torque vectoring(Taylor and Francis, 2017-09-12) Smith, Edward N.; Velenis, Efstathios; Tavernini, Davide; Cao, DongpuIn this paper, the effect of both passive and actively-modified vehicle handling characteristics on minimum time manoeuvring for vehicles with 4-wheel torque vectoring (TV) capability is studied. First, a baseline optimal TV strategy is sought, independent of any causal control law. An optimal control problem (OCP) is initially formulated considering 4 independent wheel torque inputs, together with the steering angle rate, as the control variables. Using this formulation, the performance benefit using TV against an electric drive train with a fixed torque distribution, is demonstrated. The sensitivity of TV-controlled manoeuvre time to the passive understeer gradient of the vehicle is then studied. A second formulation of the OCP is introduced where a closed-loop TV controller is incorporated into the system dynamics of the OCP. This formulation allows the effect of actively modifying a vehicle's handling characteristic via TV on its minimum time cornering performance of the vehicle to be assessed. In particular, the effect of the target understeer gradient as the key tuning parameter of the literature-standard steady-state linear single-track model yaw rate reference is analysed.Item Open Access Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective(IEEE, 2017-06-16) Marina Martinez, Clara; Hu, Xiaosong; Cao, Dongpu; Velenis, Efstathios; Gao, Bo; Wellers, MatthiasPlug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.Item 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, EfstathiosWith 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.Item Open Access Evaluation of optimal yaw rate reference for closed-loop electric vehicle torque vectoring(Unknown, 2016-09-30) Smith, Edward N.; Tavernini, Davide; Velenis, Efstathios; Cao, DongpuThis work evaluates the intrinsic contribution of the yaw rate reference to the overall handling performance of an electric vehicle with torque vectoring control - in terms of minimum-time manoeuvring. A range of yaw rate references are compared through optimal control simulations incorporating closed-loop controller dynamics. Results show yaw rate reference has a significant effect on manoeuvre time.Item 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-KwongPrompted 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.Item 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, DongpuHigh 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.Item 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, YahuiPrior 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.