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The goal of automatic parking system is to accomplish the vehicle parking to the specified space automatically. It mainly includes parking space recognition, parking space matching, and trajectory generation. It has been developed enormously, but it is still a challenging work due to parking space recognition error and trajectory generation for vehicle nonparallel initial state with parking space. In this study, the authors propose multi-sensor information ensemble for parking space recognition and adaptive trajectory generation method, which is also robust to vehicle nonparallel initial state. Both simulation and real vehicle experiments are conducted to prove that the proposed method can improve the automatic parking system performance.
Changhao Piao; Jun Zhang; Kyunghi Chang; Yan Li; Mingjie Liu. Multi-Sensor Information Ensemble-Based Automatic Parking System for Vehicle Parallel/Nonparallel Initial State. Sensors 2021, 21, 2261 .
AMA StyleChanghao Piao, Jun Zhang, Kyunghi Chang, Yan Li, Mingjie Liu. Multi-Sensor Information Ensemble-Based Automatic Parking System for Vehicle Parallel/Nonparallel Initial State. Sensors. 2021; 21 (7):2261.
Chicago/Turabian StyleChanghao Piao; Jun Zhang; Kyunghi Chang; Yan Li; Mingjie Liu. 2021. "Multi-Sensor Information Ensemble-Based Automatic Parking System for Vehicle Parallel/Nonparallel Initial State." Sensors 21, no. 7: 2261.
Lane detection is a significant technology for autonomous driving. In recent years, a number of lane detection methods have been proposed. However, the performance of fast and slim methods is not satisfactory in sophisticated scenarios and some robust methods are not fast enough. Consequently, we proposed a fast and robust lane detection method by combining a semantic segmentation network and an optical flow estimation network. Specifically, the whole research was divided into three parts: lane segmentation, lane discrimination, and mapping. In terms of lane segmentation, a robust semantic segmentation network was proposed to segment key frames and a fast and slim optical flow estimation network was used to track non-key frames. In the second part, density-based spatial clustering of applications with noise (DBSCAN) was adopted to discriminate lanes. Ultimately, we proposed a mapping method to map lane pixels from pixel coordinate system to camera coordinate system and fit lane curves in the camera coordinate system that are able to provide feedback for autonomous driving. Experimental results verified that the proposed method can speed up robust semantic segmentation network by three times at most and the accuracy fell 2% at most. In the best of circumstances, the result of the lane curve verified that the feedback error was 3%.
Sheng Lu; Zhaojie Luo; Feng Gao; Mingjie Liu; Kyunghi Chang; Changhao Piao. A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation. Sensors 2021, 21, 400 .
AMA StyleSheng Lu, Zhaojie Luo, Feng Gao, Mingjie Liu, Kyunghi Chang, Changhao Piao. A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation. Sensors. 2021; 21 (2):400.
Chicago/Turabian StyleSheng Lu; Zhaojie Luo; Feng Gao; Mingjie Liu; Kyunghi Chang; Changhao Piao. 2021. "A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation." Sensors 21, no. 2: 400.
Autonomous lane keeping system is the key technique to autonomous driving. It includes lane detection, lane tracking and control. It has been developed enormously, but it is still a challenge work due to different factors such as illumination, general hyper-parameters setting for different road condition and lane boundary correction. In addition, due to imbalance on accuracy and processing time, it is hard to conduct in embedding system. In this study, an autonomous lane keeping system is developed based on deep learning. First, a lane detection and tracking system is designed, which is robust to lane boundary correction. Especially for lane detection, a light-weight network named as LaneFCNet is proposed, which can balance accuracy and processing time. Then, lane tracking was followed by detector to improve the detection performance and create autonomous driving trajectory. Finally, to brief lane fitting problem, it was treated as ridge regression problem, which can enhance the effectiveness to the whole system. Experimental results show that our integrated lane detection and tracking system can trade off accuracy and processing time and the whole line keeping system is robust enough to autonomous driving.
Mingjie Liu; Xutao Deng; Zhen Lei; Chao Jiang; Changhao Piao. Autonomous Lane Keeping System: Lane Detection, Tracking and Control on Embedded System. Journal of Electrical Engineering & Technology 2020, 16, 569 -578.
AMA StyleMingjie Liu, Xutao Deng, Zhen Lei, Chao Jiang, Changhao Piao. Autonomous Lane Keeping System: Lane Detection, Tracking and Control on Embedded System. Journal of Electrical Engineering & Technology. 2020; 16 (1):569-578.
Chicago/Turabian StyleMingjie Liu; Xutao Deng; Zhen Lei; Chao Jiang; Changhao Piao. 2020. "Autonomous Lane Keeping System: Lane Detection, Tracking and Control on Embedded System." Journal of Electrical Engineering & Technology 16, no. 1: 569-578.
Estimating the parameters of sinusoidal signals is a fundamental problem in signal processing and in time-series analysis. Although various genetic algorithms and their hybrids have been introduced to the field, the problems pertaining to complex implementation, premature convergence, and accuracy are still unsolved. To overcome these drawbacks, an enhanced genetic algorithm (EGA) based on biological evolutionary and mathematical ecological theory is originally proposed in this study; wherein a prejudice-free selection mechanism, a two-step crossover (TSC), and an adaptive mutation strategy are designed to preserve population diversity and to maintain a synergy between convergence and search ability. In order to validate the performance, benchmark function-based studies are conducted, and the results are compared with that of the standard genetic algorithm (SGA), the particle swarm optimization (PSO), the cuckoo search (CS), and the cloud model-based genetic algorithm (CMGA). The results reveal that the proposed method outperforms the others in terms of accuracy, convergence speed, and robustness against noise. Finally, parameter estimations of real-life sinusoidal signals are performed, validating the superiority and effectiveness of the proposed method.
Chao Jiang; Pruthvi Serrao; Mingjie Liu; Chongdu Cho. An Enhanced Genetic Algorithm for Parameter Estimation of Sinusoidal Signals. Applied Sciences 2020, 10, 5110 .
AMA StyleChao Jiang, Pruthvi Serrao, Mingjie Liu, Chongdu Cho. An Enhanced Genetic Algorithm for Parameter Estimation of Sinusoidal Signals. Applied Sciences. 2020; 10 (15):5110.
Chicago/Turabian StyleChao Jiang; Pruthvi Serrao; Mingjie Liu; Chongdu Cho. 2020. "An Enhanced Genetic Algorithm for Parameter Estimation of Sinusoidal Signals." Applied Sciences 10, no. 15: 5110.
Object detection, as a fundamental task in computer vision, has been developed enormously, but is still challenging work, especially for Unmanned Aerial Vehicle (UAV) perspective due to small scale of the target. In this study, the authors develop a special detection method for small objects in UAV perspective. Based on YOLOv3, the Resblock in darknet is first optimized by concatenating two ResNet units that have the same width and height. Then, the entire darknet structure is improved by increasing convolution operation at an early layer to enrich spatial information. Both these two optimizations can enlarge the receptive filed. Furthermore, UAV-viewed dataset is collected to UAV perspective or small object detection. An optimized training method is also proposed based on collected UAV-viewed dataset. The experimental results on public dataset and our collected UAV-viewed dataset show distinct performance improvement on small object detection with keeping the same level performance on normal dataset, which means our proposed method adapts to different kinds of conditions.
Mingjie Liu; Xianhao Wang; Anjian Zhou; Xiuyuan Fu; Yiwei Ma; Changhao Piao. UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective. Sensors 2020, 20, 2238 .
AMA StyleMingjie Liu, Xianhao Wang, Anjian Zhou, Xiuyuan Fu, Yiwei Ma, Changhao Piao. UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective. Sensors. 2020; 20 (8):2238.
Chicago/Turabian StyleMingjie Liu; Xianhao Wang; Anjian Zhou; Xiuyuan Fu; Yiwei Ma; Changhao Piao. 2020. "UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective." Sensors 20, no. 8: 2238.