Robust sideslip angle observer with regional stability constraint for an uncertain singular intelligent vehicle system

2018 ◽  
Vol 12 (13) ◽  
pp. 1802-1811 ◽  
Author(s):  
Te Chen ◽  
Long Chen ◽  
Yingfeng Cai ◽  
Xing Xu
Keyword(s):  
Intelligent Vehicle ◽  
Sideslip Angle ◽  
Regional Stability ◽  
Stability Constraint ◽  
Vehicle System

Design of intelligent vehicle system based on embedded ARM

2014 ◽  
Author(s):  
Wenkai Liu ◽  
Aigong Xu ◽  
Menglong Wu ◽  
Xiaowei Dong
Keyword(s):  
Intelligent Vehicle ◽  
Vehicle System

VLDNet: Vision-based lane region detection network for intelligent vehicle system using semantic segmentation

Computing ◽  
2021 ◽  
Author(s):  
Deepak Kumar Dewangan ◽  
Satya Prakash Sahu ◽  
Bandi Sairam ◽  
Aditi Agrawal
Keyword(s):  
Semantic Segmentation ◽  
Intelligent Vehicle ◽  
Region Detection ◽  
Vehicle System

Research on active collision avoidance algorithm for intelligent vehicle based on improved artificial potential field model

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091123
Author(s):  
Chaochun Yuan ◽  
Shuofeng Weng ◽  
Jie Shen ◽  
Long Chen ◽  
Youguo He ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Superior Performance ◽  
Intelligent Vehicle ◽  
Artificial Potential Field ◽  
Sideslip Angle ◽  
Lane Changing ◽  
Model Based ◽  
Braking Process

In this article, an active collision avoidance based on improved artificial potential field is proposed to satisfy collision avoidance for intelligent vehicle. A longitudinal safety distance model based on analysis of braking process and a lane-changing safety spacing model based on minimum time of lane changing under the constraint of sideslip angle are presented. In addition, an improved artificial potential field method is introduced, which represents the influence of environmental information with artificial force. Simulation results demonstrate the superior performance of the proposed algorithm over collision avoidance for intelligent vehicle.

Design and Implementation of Intelligent Vehicle Control System Based on Camera Sensor

2013 ◽  
Vol 347-350 ◽  
pp. 666-671
Author(s):  
Shubra Deb Das ◽  
Jue Yang ◽  
Muktadir Rahman Chowdhury ◽  
Mary Cindy Ah Kioon
Keyword(s):  
Processing Speed ◽  
High Speed ◽  
Closed Loop ◽  
Image Sensor ◽  
Intelligent Vehicle ◽  
Loop Control ◽  
Vehicle System ◽  
Design Proposal ◽  
Micro Controller Unit ◽  
Excellent Stability

In this paper we propose a intelligent vehicle system design with MK60N512VMD100 as the micro-controller unit and CMOS image sensor to obtain image information of the track. Our proposed comparator circuit instead of an A/D conversion to obtain the binary image enables us to achieve a bigger field of view and a higher processing speed. A unique algorithm to extract track information and to successfully extract the black guide line is devised. We also design a PID closed-loop control to adjust the rotate speed of driving electromotor and direction of steering electromotor. The results indicate that our design proposal is feasible. The intelligent vehicle could successfully achieve a high speed, while maintaining an excellent stability.

Deep Learning-Based Speed Bump Detection Model for Intelligent Vehicle System Using Raspberry Pi

2021 ◽  
Vol 21 (3) ◽  
pp. 3570-3578
Author(s):  
Deepak Kumar Dewangan ◽  
Satya Prakash Sahu
Keyword(s):  
Deep Learning ◽  
Raspberry Pi ◽  
Intelligent Vehicle ◽  
Detection Model ◽  
Vehicle System

Research of Magnetic Field Navigation Intelligent Vehicle System Based on Difference Algorithm

2014 ◽  
Vol 678 ◽  
pp. 382-387
Author(s):  
Wu Dong ◽  
Hong Xia Bie
Keyword(s):  
Magnetic Field ◽  
Control Strategy ◽  
High Speed ◽  
Vehicle Control ◽  
Intelligent Vehicle ◽  
Magnetic Field Sensors ◽  
Pid Algorithm ◽  
The Road ◽  
Vehicle System ◽  
Navigation Scheme

To implement intelligent vehicle control system based on magnetic field detection, automation navigation scheme based on difference algorithm is proposed. The feature of magnetic field of the road is analyzed, and layout method of sensors is proposed, and signals of magnetic field sensors are gathered and filtered, and road control strategy based on difference algorithm is proposed, and steering gear is used to implement cornering of intelligent vehicle, and position PID algorithm is used to implement speed control of intelligent vehicle. The experiment result shows that intelligent vehicle using this scheme can work steadily at high speed, and has higher practicability and is worth to be propagated in many fields.

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