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Efficient Dense Spatial Pyramid Network for Lane Detection

Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yinggang Li Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Lane detection separating the roads and positioning each lane is a significant task in Autonomous-driving field. Most of existing methods take lane detection as a semantic segmentation problem, and the methods based on encoder-decoder networks achieve robust performances than traditional methods with the disadvantages of large amounts of parameters and high computational cost. To improve efficiency and better adapt to adverse environments, we propose an EDSPnet by compiling the efficient dense module of depthwise dilated separable convolution (EDD module) and dense spatial pyramid (DSP) module. The two proposed modules enable our network to make full use of the contextual information from different feature maps when compared to a chain of layers. Different from symmetrical segmentation networks, we concentrate on the encoder block and a simple decoder is designed to match the input resolution. The decoder is complemented by two methods of upsampling: bilinear interpolation and deconvolution, which guarantee the accuracy and efficiency simultaneously. Our method is evaluated on the TuSimple dataset, and the experimental results show that our network has less parameters and is still efficient even compared with the state-of-the-art semantic segmentation networks.

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Vehicle and Driver Detection on Highway Based on Cascade R-CNN

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yifei Geng Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Vehicle and driver detection in the highway scene has been a research hotspot in the field of object detection in recent years, and it is still a challenging problem in the research of traffic order and road safety. In this paper, we propose a novel end-to-end vehicle and driver detection method named VDDNet which is based on Cascade R-CNN and SENet. By introducing FPN structure and SENet attention mechanism in the backbone, the ability of the model to learn effective features is enhanced. It can improve the accuracy of detection in difficult scenes such as weak light, partial occlusion, and low picture resolution. The test results based on the database of highway traffic vehicle and drivers constructed by the Jiangsu Provincial Public Security Department. It shows that the detection method has the AP rate of 91.3% and the Recall rate of 92.4%, which demonstrates the effectiveness of the proposed method in complex highway environments.

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Towards End-to-End Face Recognition Method through Spatial Transformer

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yanfang Wu Xiaobo Lu Chen Qin School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Face recognition technology is widely applied in daily life, but in most methods, similarity or affine transformation is employed to align face images according to five facial landmarks. The face alignment module is implemented independently, thus it's difficult in end-to-end training. In this paper, the main purpose is to design a towards end-to-end trainable face recognition method based on indoor scenes. Due to that spatial transformer can implement any parametrizable transformation, we joint it with recognition network, making end-to-end training possible. Simultaneously, any prior knowledge on facial landmarks isn't required. The model jointly with spatial transformer can achieve 0.3% higher accuracy than similarity transformation. Most downsampling methods ignore the sampling theorem, making convolutional networks not shift-invariant. We replace max-pooling by MaxBlurPool in spatial transformer network, and the accuracy is improved by 0.25%.

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Finite-time Consensus for Uncertain Nonlinear Second-order Multi-agent systems

Finite-time Consensus for Uncertain Nonlinear Second-order M...

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International Workshop on Variable Structure systems, VSS

作者： Xiangyu Wang Guipu Li Shihua Li Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China

This paper studies the finite-time consensus problem of leader-follower second-order multi-agent systems with mismatched uncertain nonlinearities, which are assumed to satisfy Hölder condition. By integrating sliding-mode control, distributed adding a power integrator technology and feedback domination method together, a kind of nonlinear distributed control algorithms is proposed. Under the proposed distributed control algorithms, the followers states track the leader states accurately in finite time. The rigorous stability analysis of the closed-loop system is presented based on finite-time stability theory. Moreover, a simulation example is given, which shows the effectiveness of the proposed control scheme.

关键词： Multi-agent systems Uncertainty control design Sliding mode control Decentralized control Stability analysis

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Spatial-Temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition

Spatial-Temporal Fusion Convolutional Neural Network for Sim...

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International Conference on control, Automation, Robotics and Vision

作者： Yaocong Hu MingQi Lu Xiaobo Lu Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China

Abnormal driving behaviour is one of the leading cause of terrible traffic accidents endangering human life. Therefore, study on driving behaviour surveillance has become essential to traffic security and public management. In this paper, we conduct this promising research and employ a two stream CNN framework for video-based driving behaviour recognition, in which spatial stream CNN captures appearance information from still frames, whilst temporal stream CNN captures motion information with pre-computed optical flow displacement between a few adjacent video frames. We investigate different spatial-temporal fusion strategies to combine the intra frame static clues and inter frame dynamic clues for final behaviour recognition. So as to validate the effectiveness of the designed spatial-temporal deep learning based model, we create a simulated driving behaviour dataset, containing 1237 videos with 6 different driving behavior for recognition. Experiment result shows that our proposed method obtains noticeable performance improvements compared to the existing methods.

关键词： driving behaviour convolutional neural networks spatial-temporal deep learning fusion

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Multi-Dimensional Taylor Network (MTN)-Based Adaptive Tracking control for a Class of Nonlinear systems with Input Constraints

Multi-Dimensional Taylor Network (MTN)-Based Adaptive Tracki...

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Chinese control Conference (CCC)

作者： Yu-Qun Han Hong-Sen Yan Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China

This paper focuses on the problem of multi-dimensional Taylor network (MTN)-based adaptive tracking control for a class of nonlinear systems with input constraints. As preliminaries, the saturation is first presented by a smooth function, and then a novel MTN-backstepping-based adaptive control method is designed on the basis of the Lyapunov stability theory. It is shown that the proposed control method can guarantee that all the signals in the closed-loop system are bounded and the tracking error converges to an arbitrarily small neighborhood around the origin. Finally, one example is given to illustrate the effectiveness of the proposed design approach.

关键词： Nonlinear systems Adaptive control Lyapunov methods Backstepping Closed loop systems

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Sensor Schedu.ing in Distributed Kalman Filter for Multi-Target Tracking

Sensor Scheduling in Distributed Kalman Filter for Multi-Tar...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Lucheng Sun Ya Zhang Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China

This paper studies the design of a distributed sensor schedu.ing policy for a sensor network, in which each dynamical target can only be measured by partial sensors due to the restriction of sensor resources while each sensor requires to monitor all targets. Consensus Kalman filtering algorithm and stochastic schedu.ing strategy are applied. Firstly, a necessary condition of the observation probabilities of the targets, which can guarantee the boundedness of the expected covariance of the network, is provided. Secondly, the marginal utility of the expected covariance with respect to the observation probability is proved. Then, an algorithm is proposed to compute the optimal probabilities, which requires less complex calculations. Numerical simulations are conducted to demonstrate the performance of the proposed algorithms.

关键词： Robot sensing systems Kalman filters Time measurement Covariance matrices Optimization Processor schedu.ing

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System and walking gait design for hexapod search and rescue robot

System and walking gait design for hexapod search and rescue...

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Chinese Intelligent systems Conference, CISC 2017

作者： Yang-Yang, Chen Yingying, Huang School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China School of Electronic Science and Engineering Southeast University Nanjing2017514 China

ISBN: (纸本)9789811064982

In order to adapt to the complex disaster environment, this paper considers the system design of hexapod search and rescue robot. Such hexapod robot is suitable to different kinds of roads and obstacle, which can avoid to the shortcomings of crawler robots. This hexapod search and rescue robot includes the six foot body, voice modular, obstacle avoidance and remote monitoring function. Based on the relationship between the center of gravity and the supporting polygon, the design of the hexagon robot’s walking gait is presented. The feasibility of the hexapod search and rescue robot is verified by the prototype experiment. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Machine design

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Multi-dimensional Taylor Network(MTN)-based adaptive tracking control for a class of nonlinear systems with input constraints

Multi-dimensional Taylor Network(MTN)-based adaptive trackin...

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第37届中国控制会议

作者： Yu-Qun Han Hong-Sen Yan School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper focuses on the problem of multi-dimensional Taylor network(MTN)-based adaptive tracking control for a class of nonlinear systems with input *** preliminaries,the saturation is first presented by a smooth function,and then a novel MTN-backstepping-based adaptive control method is designed on the basis of the Lyapunov stability *** is shown that the proposed control method can guarantee that all the signals in the closed-loop system are bounded and the tracking error converges to an arbitrarily small neighborhood around the ***,one example is given to illustrate the effectiveness of the proposed design approach.

关键词： Multi-dimensional Taylor Network adaptive tracking control nonlinear systems input constraints

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Multi-dimensional Taylor Network Optimal control of the Axisymmetric Cruise Missile Flight

Multi-dimensional Taylor Network Optimal Control of the Axis...

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第三届机电控制技术与交通运输国际学术会议

作者： Yongyan Ge Hongsen Yan Lingchen Xia School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

In this paper, we present the design of the multi-dimensional Taylor network(MTN) optimal controller in the flight control of cruise missile. The MTN optimal control, which combines the classical architecture of feedback control system and the new controller structure, it is not only suitable for the analysis of the stability of the closed-loop system, but also for the control of nonlinear systems with mechanism known or unknown models. Firstly, this paper will briefly introduce the theoretical basis of the MTN optimal control. Secondly, the characteristics of the missile mathematical model and the theory of missile control will be explained, accompanied with the design of controller. Finally, the feasibility of the method is validated through numerical simulation of the PID controller, PIDNN controller and the MTN optimal controller. The results show that the MTN optimal controller has the best control effect of them.

关键词： Missile control Multi-dimensional Taylor network(MTN) controller Attitude control Optimal control

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