The design of following controller for autonomous vehicle based on leader-follower strategies

MATEC Web of Conferences ◽

10.1051/matecconf/202235503029 ◽

2022 ◽

Vol 355 ◽

pp. 03029

Author(s):

Hongyang Li ◽

Hailiang Li ◽

Ziyi Li

Keyword(s):

Autonomous Vehicle ◽

Kinematic Modeling ◽

Speed Controller ◽

Error System

Aiming at the problem of following control of autonomous vehicle, the following controller is designed based on Leader-follower strategies. First, the kinematic modeling is done. Next, Leader-follower model is used to describe the following structure and L − ϕ method is used to build error system. Then, The speed controller for the follower is designed to achieve the objectives. Finally, the simulation is done by Matlab, the results show that the controller is effective.

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Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211010569 ◽

2021 ◽

Vol 18 (3) ◽

pp. 172988142110105

Author(s):

Jnana Sai Abhishek Varma Gokaraju ◽

Weon Keun Song ◽

Min-Ho Ka ◽

Somyot Kaitwanidvilai

Keyword(s):

Neural Networks ◽

Object Recognition ◽

Convolutional Neural Networks ◽

Doppler Radar ◽

Autonomous Vehicle ◽

Sampling Point ◽

Body Segment ◽

Kinematic Modeling ◽

Deep Convolutional Neural Networks ◽

Kinematic Models

The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.

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