Browsing by Author "Wang, Hong"

Now showing 1 - 5 of 5
  • Thumbnail Image
    ItemOpen 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.
  • Thumbnail Image
    ItemOpen 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.
  • Thumbnail Image
    ItemOpen 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.
  • Thumbnail Image
    ItemOpen 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.
  • Thumbnail Image
    ItemOpen Access
    Recognizing driver braking intention with vehicle data using unsupervised learning methods
    (SAE International, 2017-03) Xing, Yang; Lv, Chen; Huaji, Wang; Wang, Hong; Cao, Dongpu
    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 response to traffic situation correctly and improve the driving experience of intelligent vehicles. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. In this paper, a various unsupervised clustering methods will be used to build a driver braking intention predictor which can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different methods like K-means and Gaussian mixture model will be compared. Additionally, the evaluation of features from raw data, which are important to driving maneuvers clustering will be proposed. The experiment data are collected from one hybrid electric vehicle in real world. Final results show that the proposed method can detect driver’s braking intention in a very beginning moment with a high accuracy and the most important sets of feature for driving maneuver clustering will be discussed.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 response to traffic situation correctly and improve the driving experience of intelligent vehicles. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. In this paper, a various unsupervised clustering methods will be used to build a driver braking intention predictor which can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different methods like K-means and Gaussian mixture model will be compared. Additionally, the evaluation of features from raw data, which are important to driving maneuvers clustering will be proposed. The experiment data are collected from one hybrid electric vehicle in real world. Final results show that the proposed method can detect driver’s braking intention in a very beginning moment with a high accuracy and the most important sets of feature for driving maneuver clustering will be discussed.