The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform

Zhenchao Ouyang - One of the best experts on this subject based on the ideXlab platform.

  • Deep CNN-Based Real-Time Traffic Light Detector for Self-Driving Vehicles
    IEEE Transactions on Mobile Computing, 2020
    Co-Authors: Zhenchao Ouyang, Mohsen Guizani
    Abstract:

    Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) is still considered an important module in autonomous vehicles and Driver Assistance Systems (DAS). To overcome low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a Lightweight and real-time traffic Light Detector for the autonomous vehicle platform. Our model consists of a heuristic candidate region selection module to identify all possible traffic Lights, and a Lightweight Convolution Neural Network (CNN) classifier to classify the results obtained. Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption. By integrating our Detector module on NVidia Jetson TX1/TX2, we conduct on-road tests on two full-scale self-driving vehicle platforms (a car and a bus) in normal traffic conditions. Our model can achieve an average detection accuracy of 99.3 percent (mRttld) and 99.7 percent (Rttld) at 10Hz on TX1 and TX2, respectively. The on-road tests also show that our traffic Light detection module can achieve $ ± 1 . 5 m errors at stop lines when working with other self-driving modules.

  • Deep CNN-Based Real-Time Traffic Light Detector for Self-Driving Vehicles
    IEEE Transactions on Mobile Computing, 2020
    Co-Authors: Zhenchao Ouyang, Mohsen Guizani
    Abstract:

    Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) is still considered an important module in autonomous vehicles and Driver Assistance Systems (DAS). To overcome low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a Lightweight and real-time traffic Light Detector for the autonomous vehicle platform. Our model consists of a heuristic candidate region selection module to identify all possible traffic Lights, and a Lightweight Convolution Neural Network (CNN) classifier to classify the results obtained. Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption. By integrating our Detector module on NVidia Jetson TX1/TX2, we conduct on-road tests on two full-scale self-driving vehicle platforms (a car and a bus) in normal traffic conditions. Our model can achieve an average detection accuracy of 99.3 percent (mRttld) and 99.7 percent (Rttld) at 10Hz on TX1 and TX2, respectively. The on-road tests also show that our traffic Light detection module can achieve

  • Deep CNN-based Real-time Traffic Light Detector for Self-driving Vehicles
    IEEE Transactions on Mobile Computing, 2019
    Co-Authors: Jianwei Niu, Yu Liu, Mohsen Guizani, Zhenchao Ouyang
    Abstract:

    Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) still is considered an important module in autonomous vehicles and Driver Assistance Systems (DAS). Considering low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a Lightweight and real-time traffic Light Detector for the autonomous vehicle platform on the basis of previous studies. Our model consists of a heuristic candidate region selection module to identify all possible traffic Lights, and a Lightweight Convolution Neural Network (CNN) classifier to classify the results obtained. Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption. By integrating our Detector module on NVidia Jetson TX1/TX2, we conduct on-road tests on two full scale self-driving vehicle platforms (a car and a bus) in normal traffic conditions. The final model can achieve average detection accuracy of 99.3% (mRttld) and 99.7% (Rttld) at 10Hz on TX1 and TX2, respectively. The on-road tests also show that our traffic Light detection module can achieve < ±1.5m errors at stop lines when working with other self-driving modules.

Mohsen Guizani - One of the best experts on this subject based on the ideXlab platform.

  • Deep CNN-Based Real-Time Traffic Light Detector for Self-Driving Vehicles
    IEEE Transactions on Mobile Computing, 2020
    Co-Authors: Zhenchao Ouyang, Mohsen Guizani
    Abstract:

    Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) is still considered an important module in autonomous vehicles and Driver Assistance Systems (DAS). To overcome low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a Lightweight and real-time traffic Light Detector for the autonomous vehicle platform. Our model consists of a heuristic candidate region selection module to identify all possible traffic Lights, and a Lightweight Convolution Neural Network (CNN) classifier to classify the results obtained. Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption. By integrating our Detector module on NVidia Jetson TX1/TX2, we conduct on-road tests on two full-scale self-driving vehicle platforms (a car and a bus) in normal traffic conditions. Our model can achieve an average detection accuracy of 99.3 percent (mRttld) and 99.7 percent (Rttld) at 10Hz on TX1 and TX2, respectively. The on-road tests also show that our traffic Light detection module can achieve $ ± 1 . 5 m errors at stop lines when working with other self-driving modules.

  • Deep CNN-Based Real-Time Traffic Light Detector for Self-Driving Vehicles
    IEEE Transactions on Mobile Computing, 2020
    Co-Authors: Zhenchao Ouyang, Mohsen Guizani
    Abstract:

    Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) is still considered an important module in autonomous vehicles and Driver Assistance Systems (DAS). To overcome low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a Lightweight and real-time traffic Light Detector for the autonomous vehicle platform. Our model consists of a heuristic candidate region selection module to identify all possible traffic Lights, and a Lightweight Convolution Neural Network (CNN) classifier to classify the results obtained. Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption. By integrating our Detector module on NVidia Jetson TX1/TX2, we conduct on-road tests on two full-scale self-driving vehicle platforms (a car and a bus) in normal traffic conditions. Our model can achieve an average detection accuracy of 99.3 percent (mRttld) and 99.7 percent (Rttld) at 10Hz on TX1 and TX2, respectively. The on-road tests also show that our traffic Light detection module can achieve

  • Deep CNN-based Real-time Traffic Light Detector for Self-driving Vehicles
    IEEE Transactions on Mobile Computing, 2019
    Co-Authors: Jianwei Niu, Yu Liu, Mohsen Guizani, Zhenchao Ouyang
    Abstract:

    Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) still is considered an important module in autonomous vehicles and Driver Assistance Systems (DAS). Considering low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a Lightweight and real-time traffic Light Detector for the autonomous vehicle platform on the basis of previous studies. Our model consists of a heuristic candidate region selection module to identify all possible traffic Lights, and a Lightweight Convolution Neural Network (CNN) classifier to classify the results obtained. Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption. By integrating our Detector module on NVidia Jetson TX1/TX2, we conduct on-road tests on two full scale self-driving vehicle platforms (a car and a bus) in normal traffic conditions. The final model can achieve average detection accuracy of 99.3% (mRttld) and 99.7% (Rttld) at 10Hz on TX1 and TX2, respectively. The on-road tests also show that our traffic Light detection module can achieve < ±1.5m errors at stop lines when working with other self-driving modules.

K Sreenivas - One of the best experts on this subject based on the ideXlab platform.

  • low intensity ultraviolet Light Detector using a surface acoustic wave oscillator based on zno linbo3 bilayer structure
    Semiconductor Science and Technology, 2005
    Co-Authors: Sanjeev Kumar, Parmanand Sharma, K Sreenivas
    Abstract:

    A surface acoustic wave (SAW) based ultraviolet (UV) Light Detector configured in the form of a SAW oscillator is investigated. The SAW oscillator consists of a ZnO/LiNbO3 bilayer structure, where ZnO is used as UV Light sensing material and LiNbO3 is used to excite the surface acoustic waves. The SAW oscillator is characterized for its functional performance at different Vcc, the voltage to the oscillator circuit, under different levels of UV Light illumination. The changes in the amplitude and the frequency shift of the SAW oscillator output exhibit a linear variation with UV Light intensity. The SAW UV Detector is shown to be highly sensitive at lower Vcc. A low level UV intensity of 450 nW cm?2 is easily detectable and the voltage responsivity is estimated to be ~24 kV W?1.

  • Low-intensity ultraviolet Light Detector using a surface acoustic wave oscillator based on ZnO/LiNbO3 bilayer structure
    Semiconductor Science and Technology, 2005
    Co-Authors: Sanjeev Kumar, Parmanand Sharma, K Sreenivas
    Abstract:

    A surface acoustic wave (SAW) based ultraviolet (UV) Light Detector configured in the form of a SAW oscillator is investigated. The SAW oscillator consists of a ZnO/LiNbO3 bilayer structure, where ZnO is used as UV Light sensing material and LiNbO3 is used to excite the surface acoustic waves. The SAW oscillator is characterized for its functional performance at different Vcc, the voltage to the oscillator circuit, under different levels of UV Light illumination. The changes in the amplitude and the frequency shift of the SAW oscillator output exhibit a linear variation with UV Light intensity. The SAW UV Detector is shown to be highly sensitive at lower Vcc. A low level UV intensity of 450 nW cm?2 is easily detectable and the voltage responsivity is estimated to be ~24 kV W?1.

Sanjeev Kumar - One of the best experts on this subject based on the ideXlab platform.

  • low intensity ultraviolet Light Detector using a surface acoustic wave oscillator based on zno linbo3 bilayer structure
    Semiconductor Science and Technology, 2005
    Co-Authors: Sanjeev Kumar, Parmanand Sharma, K Sreenivas
    Abstract:

    A surface acoustic wave (SAW) based ultraviolet (UV) Light Detector configured in the form of a SAW oscillator is investigated. The SAW oscillator consists of a ZnO/LiNbO3 bilayer structure, where ZnO is used as UV Light sensing material and LiNbO3 is used to excite the surface acoustic waves. The SAW oscillator is characterized for its functional performance at different Vcc, the voltage to the oscillator circuit, under different levels of UV Light illumination. The changes in the amplitude and the frequency shift of the SAW oscillator output exhibit a linear variation with UV Light intensity. The SAW UV Detector is shown to be highly sensitive at lower Vcc. A low level UV intensity of 450 nW cm?2 is easily detectable and the voltage responsivity is estimated to be ~24 kV W?1.

  • Low-intensity ultraviolet Light Detector using a surface acoustic wave oscillator based on ZnO/LiNbO3 bilayer structure
    Semiconductor Science and Technology, 2005
    Co-Authors: Sanjeev Kumar, Parmanand Sharma, K Sreenivas
    Abstract:

    A surface acoustic wave (SAW) based ultraviolet (UV) Light Detector configured in the form of a SAW oscillator is investigated. The SAW oscillator consists of a ZnO/LiNbO3 bilayer structure, where ZnO is used as UV Light sensing material and LiNbO3 is used to excite the surface acoustic waves. The SAW oscillator is characterized for its functional performance at different Vcc, the voltage to the oscillator circuit, under different levels of UV Light illumination. The changes in the amplitude and the frequency shift of the SAW oscillator output exhibit a linear variation with UV Light intensity. The SAW UV Detector is shown to be highly sensitive at lower Vcc. A low level UV intensity of 450 nW cm?2 is easily detectable and the voltage responsivity is estimated to be ~24 kV W?1.

Rami Botros - One of the best experts on this subject based on the ideXlab platform.

  • A deep learning approach to traffic Lights: Detection, tracking, and classification
    Proceedings - IEEE International Conference on Robotics and Automation, 2017
    Co-Authors: Karsten Behrendt, Libor Novak, Rami Botros
    Abstract:

    — Reliable traffic Light detection and classification is crucial for automated driving in urban environments. Cur-rently, there are no systems that can reliably perceive traffic Lights in real-time, without map-based information, and in sufficient distances needed for smooth urban driving. We propose a complete system consisting of a traffic Light Detector, tracker, and classifier based on deep learning, stereo vision, and vehicle odometry which perceives traffic Lights in real-time. Within the scope of this work, we present three major contributions. The first is an accurately labeled traffic Light dataset of 5000 images for training and a video sequence of 8334 frames for evaluation. The dataset is published as the Bosch Small Traffic Lights Dataset and uses our results as baseline. It is currently the largest publicly available labeled traffic Light dataset and includes labels down to the size of only 1 pixel in width. The second contribution is a traffic Light Detector which runs at 10 frames per second on 1280×720 images. When selecting the confidence threshold that yields equal error rate, we are able to detect traffic Lights as small as 4 pixels in width. The third contribution is a traffic Light tracker which uses stereo vision and vehicle odometry to compute the motion estimate of traffic Lights and a neural network to correct the aforementioned motion estimate.