Night Vision Pedestrian Warning intelligent vehicle (IV) night vision pedestrian warning in Intelligent Vehicles intelligent vehicle (IV)

2012 ◽  
pp. 6950-6975
Author(s):  
Matthias Serfling ◽  
Otto Löhlein
Keyword(s):  
Intelligent Vehicles ◽  
Night Vision ◽  
Intelligent Vehicle

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

Blockchain-Autonomous Driving Systems

2021 ◽  
pp. 87-114
Author(s):  
P. Lalitha Surya Kumari
Keyword(s):  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Autonomous Driving ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Privacy And Security ◽  
Trust Network ◽  
Vehicle Communication ◽  
Blockchain Technology ◽  
Vehicle Data

Blockchain is the upcoming new information technology that could have quite a lot of significant future applications. In this chapter, the communication network for the reliable environment of intelligent vehicle systems is considered along with how the blockchain technology generates trust network among intelligent vehicles. It also discusses different factors that are effecting or motivating automotive industry, data-driven intelligent transportation system (D2ITS), structure of VANET, framework of intelligent vehicle data sharing based on blockchain used for intelligent vehicle communication and decentralized autonomous vehicles (DAV) network. It also talks about the different ways the autonomous vehicles use blockchain. Block-VN distributed architecture is discussed in detail. The different challenges of research and privacy and security of vehicular network are discussed.

scholarly journals The Negative Impact of Vehicular Intelligence on Energy Consumption

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Zongwei Liu ◽  
Hong Tan ◽  
Xu Kuang ◽  
Han Hao ◽  
Fuquan Zhao
Keyword(s):  
Energy Consumption ◽  
Negative Impact ◽  
Communication Technologies ◽  
High Energy ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Computing Platform ◽  
Car Market ◽  
Emissions Mitigation ◽  
Consumption Cost

The development of intelligent vehicle will provide the Chinese automotive industry with a strategic opportunity for transformation and upgrading. Vehicular intelligence provides new solutions for energy conservation and emissions mitigation. However, the process of vehicular intelligence is progressive. The saving of energy consumption depends on the high smart car market penetration rate. But one thing that can be confirmed is that intelligent vehicles are equipped with advanced sensors, controllers, and actuators, in combination with connecting communication technologies compared with conventional vehicles, for which the energy consumption of the vehicle will definitely increase. In this study, vehicle fuel consumption cost at different levels of intelligence is calculated, considering the energy consumption of hardware used for automation and connecting functions, the energy consumption cost generated by the quality of the hardware, and the wind resistance. The results reveal that the energy consumption per 100 kilometers of an intelligent vehicle ranges from 0.78L to 1.86L, more than traditional vehicle. The energy consumption cost of automation functions is much higher than that of the connecting functions. Computing platform performance, connection strength, and radar performance are the three main factors that affect energy consumption cost. Based on the analysis, the high energy consumption cost of vehicular intelligence has a profound impact on choosing power platform.

scholarly journals Crowd-Sourced Mapping of New Feature Layer for High-Definition Map

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4172 ◽  
Author(s):  
Chansoo Kim ◽  
Sungjin Cho ◽  
Myoungho Sunwoo ◽  
Kichun Jo
Keyword(s):  
Conventional Method ◽  
Mobile Network ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Network System ◽  
High Definition ◽  
The Public ◽  
Core Technology ◽  
Mapping Process ◽  
New Feature

A High-Definition map (HD map) is a precise and detailed map composed of various landmark feature layers. The HD map is a core technology that facilitates the essential functions of intelligent vehicles. Recently, it has come to be required for the HD map to continuously add new feature layers in order to increase the performances of intelligent vehicles in more complicated environments. However, it is difficult to generate a new feature layer for the HD map, because the conventional method of generating the HD map based on several professional mapping cars has high costs in terms of time and money due to the need to re-drive on all of the public roads. In order to reduce these costs, we propose a crowd-sourced mapping process of the new feature layer for the HD map. This process is composed of two steps. First, new features in the environments are acquired from multiple intelligent vehicles. The acquired new features build each new feature layer in each intelligent vehicle using the HD map-based GraphSLAM approach, and these new feature layers are conveyed to a map cloud through a mobile network system. Next, the crowd-sourced new feature layers are integrated into a new feature layer in a map cloud. In the simulation, the performance of the crowd-sourced process is then analyzed and evaluated. Experiments in real driving environments confirm the results of the simulation.

Electromagnetic Identification Intelligent Vehicle System Design

2013 ◽  
Vol 401-403 ◽  
pp. 1695-1698
Author(s):  
Yong Gang Ji ◽  
Han Ming Zheng ◽  
Yan Peng Zhang
Keyword(s):  
System Design ◽  
Software Design ◽  
Intelligent Vehicles ◽  
Design Flow ◽  
Intelligent Vehicle ◽  
Motor Drive ◽  
Electromagnetic Signal ◽  
Specific Design ◽  
Hardware System ◽  
Design Diagram

This paper introduces the electromagnetic identification intelligent vehicles hardware system design diagram and software design process based MC9S12XSl28 microcontroller, focusing on the design of intelligent vehicle power unit circuit, the electromagnetic signal amplification circuit, the motor drive module, gives a detailed schematic. Software design give a specific design flow, integrated hardware and software design constitutes intelligent vehicles overall system.

scholarly journals Cloud Model Approach for Lateral Control of Intelligent Vehicle Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Hongbo Gao ◽  
Xinyu Zhang ◽  
Yuchao Liu ◽  
Deyi Li
Keyword(s):  
Control Algorithm ◽  
Cloud Model ◽  
Control Area ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Control Effect ◽  
Lateral Control ◽  
Direction Control ◽  
Vehicle Test ◽  
Reasoning Algorithm

Studies on intelligent vehicles, among which the controlling method of intelligent vehicles is a key technique, have drawn the attention of industry and the academe. This study focuses on designing an intelligent lateral control algorithm for vehicles at various speeds, formulating a strategy, introducing the Gauss cloud model and the cloud reasoning algorithm, and proposing a cloud control algorithm for calculating intelligent vehicle lateral offsets. A real vehicle test is applied to explain the implementation of the algorithm. Empirical results show that if the Gauss cloud model and the cloud reasoning algorithm are applied to calculate the lateral control offset and the vehicles drive at different speeds within a direction control area of ±7°, a stable control effect is achieved.

Research on Embedded Intelligent Vehicle Tracking Avoidance of Obstacle

2014 ◽  
Vol 1044-1045 ◽  
pp. 926-929
Author(s):  
Yan Yan Cheng ◽  
Quan Bo Yuan
Keyword(s):  
Obstacle Avoidance ◽  
Intelligent Vehicles ◽  
Vehicle Tracking ◽  
Intelligent Vehicle ◽  
Automotive Applications ◽  
Automatic Tracking

This paper describes the development and widely cited robots for intelligent vehicles unmanned automotive applications, smart car automatic tracking and avoidance were studied to select the appropriate tracking and obstacle avoidance algorithm, used to embed smart car achieve its tracking avoidance function..

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