The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform

Chong-an Wang - One of the best experts on this subject based on the ideXlab platform.

  • Vehicle Safety Distance Warning System: A Novel Algorithm for Vehicle Safety Distance Calculating Between Moving Cars
    2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, 2007
    Co-Authors: Yuan-lin Chen, Chong-an Wang
    Abstract:

    A novel algorithm for Vehicle Safety distance between driving cars for Vehicle Safety warning system is presented in this paper. The presented system concept includes a distance obstacle detection and Safety distance calculation. The system detects the distance between the car and the in front of Vehicles (obstacles) and uses the Vehicle speed and other parameters to calculate the braking Safety distance of the moving car. The system compares the obstacle distance and braking Safety distance which are used to determine the moving Vehicle's Safety distance is enough or not. This paper focuses on the solution algorithm presentation.

  • VTC Spring - Vehicle Safety Distance Warning System: A Novel Algorithm for Vehicle Safety Distance Calculating Between Moving Cars
    2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, 2007
    Co-Authors: Yuan-lin Chen, Chong-an Wang
    Abstract:

    A novel algorithm for Vehicle Safety distance between driving cars for Vehicle Safety warning system is presented in this paper. The presented system concept includes a distance obstacle detection and Safety distance calculation. The system detects the distance between the car and the in front of Vehicles (obstacles) and uses the Vehicle speed and other parameters to calculate the braking Safety distance of the moving car. The system compares the obstacle distance and braking Safety distance which are used to determine the moving Vehicle's Safety distance is enough or not. This paper focuses on the solution algorithm presentation.

Yuan-lin Chen - One of the best experts on this subject based on the ideXlab platform.

  • Vehicle Safety Distance Warning System: A Novel Algorithm for Vehicle Safety Distance Calculating Between Moving Cars
    2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, 2007
    Co-Authors: Yuan-lin Chen, Chong-an Wang
    Abstract:

    A novel algorithm for Vehicle Safety distance between driving cars for Vehicle Safety warning system is presented in this paper. The presented system concept includes a distance obstacle detection and Safety distance calculation. The system detects the distance between the car and the in front of Vehicles (obstacles) and uses the Vehicle speed and other parameters to calculate the braking Safety distance of the moving car. The system compares the obstacle distance and braking Safety distance which are used to determine the moving Vehicle's Safety distance is enough or not. This paper focuses on the solution algorithm presentation.

  • VTC Spring - Vehicle Safety Distance Warning System: A Novel Algorithm for Vehicle Safety Distance Calculating Between Moving Cars
    2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, 2007
    Co-Authors: Yuan-lin Chen, Chong-an Wang
    Abstract:

    A novel algorithm for Vehicle Safety distance between driving cars for Vehicle Safety warning system is presented in this paper. The presented system concept includes a distance obstacle detection and Safety distance calculation. The system detects the distance between the car and the in front of Vehicles (obstacles) and uses the Vehicle speed and other parameters to calculate the braking Safety distance of the moving car. The system compares the obstacle distance and braking Safety distance which are used to determine the moving Vehicle's Safety distance is enough or not. This paper focuses on the solution algorithm presentation.

Yan Chen - One of the best experts on this subject based on the ideXlab platform.

  • Guaranteed Vehicle Safety Control Using Control-Dependent Barrier Functions
    2019 American Control Conference (ACC), 2019
    Co-Authors: Yiwen Huang, Sze Zheng Yong, Yan Chen
    Abstract:

    How to guarantee the Safety of autonomous ground Vehicles is still a significant challenge, although different active Safety control systems have been developed, including techniques based on controlled invariant sets and control barrier functions (CBF) that have been proven to guarantee the Safety of dynamic systems. To apply these techniques to Vehicle Safety control, we regarded an estimated lateral stability region of a Vehicle as a controlled invariant set. However, the stability region was found to vary with respect to the system control input, which is normally the steering angle. Therefore, the existing definition of the controlled invariant set, which is independent of control inputs, may not be applicable. In this paper, the definition of a controlled invariant set is extended to a control-dependent invariant set, so that the controlled invariant set can vary with control inputs. Based on the extended definition, a new invariance condition using control-dependent barrier function (CDBF) is proposed and applied to the guaranteed Vehicle Safety control problem. Finally, the guaranteed Vehicle Safety control is demonstrated by simulation results.

  • ACC - Guaranteed Vehicle Safety Control Using Control-Dependent Barrier Functions
    2019 American Control Conference (ACC), 2019
    Co-Authors: Yiwen Huang, Sze Zheng Yong, Yan Chen
    Abstract:

    How to guarantee the Safety of autonomous ground Vehicles is still a significant challenge, although different active Safety control systems have been developed, including techniques based on controlled invariant sets and control barrier functions (CBF) that have been proven to guarantee the Safety of dynamic systems. To apply these techniques to Vehicle Safety control, we regarded an estimated lateral stability region of a Vehicle as a controlled invariant set. However, the stability region was found to vary with respect to the system control input, which is normally the steering angle. Therefore, the existing definition of the controlled invariant set, which is independent of control inputs, may not be applicable. In this paper, the definition of a controlled invariant set is extended to a control-dependent invariant set, so that the controlled invariant set can vary with control inputs. Based on the extended definition, a new invariance condition using control-dependent barrier function (CDBF) is proposed and applied to the guaranteed Vehicle Safety control problem. Finally, the guaranteed Vehicle Safety control is demonstrated by simulation results.

Yiwen Huang - One of the best experts on this subject based on the ideXlab platform.

  • Guaranteed Vehicle Safety Control Using Control-Dependent Barrier Functions
    2019 American Control Conference (ACC), 2019
    Co-Authors: Yiwen Huang, Sze Zheng Yong, Yan Chen
    Abstract:

    How to guarantee the Safety of autonomous ground Vehicles is still a significant challenge, although different active Safety control systems have been developed, including techniques based on controlled invariant sets and control barrier functions (CBF) that have been proven to guarantee the Safety of dynamic systems. To apply these techniques to Vehicle Safety control, we regarded an estimated lateral stability region of a Vehicle as a controlled invariant set. However, the stability region was found to vary with respect to the system control input, which is normally the steering angle. Therefore, the existing definition of the controlled invariant set, which is independent of control inputs, may not be applicable. In this paper, the definition of a controlled invariant set is extended to a control-dependent invariant set, so that the controlled invariant set can vary with control inputs. Based on the extended definition, a new invariance condition using control-dependent barrier function (CDBF) is proposed and applied to the guaranteed Vehicle Safety control problem. Finally, the guaranteed Vehicle Safety control is demonstrated by simulation results.

  • ACC - Guaranteed Vehicle Safety Control Using Control-Dependent Barrier Functions
    2019 American Control Conference (ACC), 2019
    Co-Authors: Yiwen Huang, Sze Zheng Yong, Yan Chen
    Abstract:

    How to guarantee the Safety of autonomous ground Vehicles is still a significant challenge, although different active Safety control systems have been developed, including techniques based on controlled invariant sets and control barrier functions (CBF) that have been proven to guarantee the Safety of dynamic systems. To apply these techniques to Vehicle Safety control, we regarded an estimated lateral stability region of a Vehicle as a controlled invariant set. However, the stability region was found to vary with respect to the system control input, which is normally the steering angle. Therefore, the existing definition of the controlled invariant set, which is independent of control inputs, may not be applicable. In this paper, the definition of a controlled invariant set is extended to a control-dependent invariant set, so that the controlled invariant set can vary with control inputs. Based on the extended definition, a new invariance condition using control-dependent barrier function (CDBF) is proposed and applied to the guaranteed Vehicle Safety control problem. Finally, the guaranteed Vehicle Safety control is demonstrated by simulation results.

Pierre Loonis - One of the best experts on this subject based on the ideXlab platform.

  • ITSC - Evidential model and hierarchical information fusion framework for Vehicle Safety evaluation
    17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014
    Co-Authors: Xuanpeng Li, Emmanuel Seignez, Dominique Gruyer, Pierre Loonis
    Abstract:

    Vehicle Safety evaluation is a systematic and comprehensive process involving Vehicles, road environments, and driver behaviours. In real road conditions, due to great uncertainty, evaluation based on singular information source lacks in sufficient accuracy and stability. In this paper, the authors proposed a vision-based real-time Vehicle Safety evaluation system using lane and driver's eye information, which were modelled in the framework of evidence theory. Vehicle Safety was assessed via hierarchical fusion of driver drowsiness detection and distracted and impaired driving performance. The system was validated in real world scenarios. Experimental results demonstrate that it is promising to improve the robustness and temporal response of vigilance of Vehicle Safety.

  • Vehicle Safety evaluation based on driver drowsiness and distracted and impaired driving performance using evidence theory
    2014 IEEE Intelligent Vehicles Symposium Proceedings, 2014
    Co-Authors: Xuanpeng Li, Emmanuel Seignez, Wenjie Lu, Pierre Loonis
    Abstract:

    Vehicle Safety is the study and practice for minimizing the occurrences and consequences of traffic accidents. It is found that driver behaviors such as drowsiness, impaired driving and distraction are contributing factors to traffic accidents. In complex road surroundings, comprehensive analysis is more robust than separate evaluations which are broadly proceeded with. In this paper, we propose a vision-based nonintrusive system involving lane and driver's eye features to analyze driver behaviors. In the framework of evidence theory, evaluations of driver drowsiness and distracted and impaired driving performance are integrated to evaluate Vehicle Safety in real time. The system was validated in real world scenarios, and experimental results demonstrate that it is promising to improve the robustness and temporal response of Vehicle Safety vigilance.

  • Evidential model and hierarchical information fusion framework for Vehicle Safety evaluation
    17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014
    Co-Authors: Xuanpeng Li, Emmanuel Seignez, Dominique Gruyer, Pierre Loonis
    Abstract:

    Vehicle Safety evaluation is a systematic and comprehensive process involving Vehicles, road environments, and driver behaviours. In real road conditions, due to great uncertainty, evaluation based on singular information source lacks in sufficient accuracy and stability. In this paper, we proposed a vision-based real-time Vehicle Safety evaluation system using lane and driver's eye information, which were modelled in the framework of evidence theory. Vehicle Safety was assessed via hierarchical fusion of driver drowsiness detection and distracted and impaired driving performance. The system was validated in real world scenarios. Experimental results demonstrate that it is promising to improve the robustness and temporal response of vigilance of Vehicle Safety.