scholarly journals Robust Cooperative Multi-Vehicle Tracking with Inaccurate Self-Localization Based on On-Board Sensors and Inter-Vehicle Communication

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3212 ◽  
Author(s):  
Xiaobo Chen ◽  
Jianyu Ji ◽  
Yanjun Wang
Keyword(s):  
Intelligent Vehicles ◽  
Vehicle Tracking ◽  
Vehicle Communication ◽  
Bayes Inference ◽  
Surrounding Environment ◽  
Covariance Intersection ◽  
Three Stages ◽  
Cooperative Tracking ◽  
Cooperative Vehicles ◽  
Localization Information

The fusion of on-board sensors and transmitted information via inter-vehicle communication has been proved to be an effective way to increase the perception accuracy and extend the perception range of connected intelligent vehicles. The current approaches rely heavily on the accurate self-localization of both host and cooperative vehicles. However, such information is not always available or accurate enough for effective cooperative sensing. In this paper, we propose a robust cooperative multi-vehicle tracking framework suitable for the situation where the self-localization information is inaccurate. Our framework consists of three stages. First, each vehicle perceives its surrounding environment based on the on-board sensors and exchanges the local tracks through inter-vehicle communication. Then, an algorithm based on Bayes inference is developed to match the tracks from host and cooperative vehicles and simultaneously optimize the relative pose. Finally, the tracks associated with the same target are fused by fast covariance intersection based on information theory. The simulation results based on both synthesized data and a high-quality physics-based platform show that our approach successfully implements cooperative tracking without the assistance of accurate self-localization.

Towards the Development of Vehicular Ad-Hoc Networks (VANETs)

2020 ◽  
pp. 21-47 ◽  
Author(s):  
Mekelleche Fatiha ◽  
Haffaf Hafid
Keyword(s):  
Ad Hoc Networks ◽  
Intelligent Transportation Systems ◽  
Road Safety ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Intelligent Vehicles ◽  
The Road ◽  
Vehicle Communication ◽  
On The Road ◽  
Hoc Networks

Vehicular Ad-Hoc Networks (VANETs), a new mobile ad-hoc network technology (MANET), are currently receiving increased attention from manufacturers and researchers. They consist of several mobile vehicles (intelligent vehicles) that can communicate with each other (inter-vehicle communication) or with fixed road equipment (vehicle-infrastructure communication) adopting new wireless communication technologies. The objective of these networks is to improve road safety by warning motorists of any event on the road (accidents, hazards, possible deviations, etc.), and make the time spent on the road more pleasant and less boring (applications deployed to ensure the comfort of the passengers). Practically, VANETs are designed to support the development of Intelligent Transportation Systems (ITS). The latter are seen as one of the technical solutions to transport challenges. This chapter, given the importance of road safety in the majority of developed countries, presents a comprehensive study on the VANET networks, highlighting their main features.

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.

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..

scholarly journals Tibetan Buddhism and the formation of ecological consciousness: a view from Buryatia

2018 ◽  
Vol 55 ◽  
pp. 05004
Author(s):  
K. Bagaeva ◽  
D. Tsyrendorzhieva ◽  
O. Balchindorzhieva ◽  
M. Badmaeva
Keyword(s):  
Tibetan Buddhism ◽  
Ecological Civilization ◽  
Ecological Consciousness ◽  
Basic Principles ◽  
Surrounding Environment ◽  
The World ◽  
Human Understanding ◽  
Universal Dependence ◽  
Three Stages ◽  
Living Nature

Technicization of human and society, active development of technogenic civilization leads to gradual separation from moral values and principles. These values include ideas of unity and harmony of human with nature, with the surrounding environment, and reasonable, moderate attitude towards natural resources. We believe that humanity should move from the industrial to ecological civilization. The foundations of a new ecophilosophy should become holistic principles, representations of philosophy in general and Buddhism in particular. We outlined basic principles and methods for improving personality of altruistic ethics of Mahayana Buddhism that contribute to human understanding of inseparability, interconnection with the world. We focus on the central Buddhist concept – the absence of an individual ‘I’ that is understood as necessity of recognizing oneself as a separate empirical individual. That is confirmed by a translation of the text PrajnaParamitaHridaya Sutra from Tibetan language. The paper analyzes three types of spiritual personality that correspond to three stages of the Path to awakening. Each stage is a step towards the formation of subjectless consciousness, that is, awareness of universal dependence and responsibility for their actions. The paper argues that for ecological consciousness it is important to form an understanding that the main reason for human existence in not the technosphere, not the economy, but the living nature.

scholarly journals SOSIALISASI KEBIJAKAN KAWASAN TANPA ROKOK DI RSUD LUBUK PAKAM DELI SERDANG

2021 ◽  
Vol 1 (1) ◽  
pp. 208-212
Author(s):  
Balqis Wasliati ◽  
Ika Nur Saputri ◽  
Delita Br Panjaitan ◽  
Raisha Octavariny ◽  
Christina Octavia
Keyword(s):  
Health Workers ◽  
Health Impacts ◽  
Ministry Of Health ◽  
The Public ◽  
Surrounding Environment ◽  
Three Stages ◽  
Implementation Method ◽  
The Government ◽  
Smoking Control ◽  
And Control

Non-Smoking Area (KTR) is a policy made by the Ministry of Health to prevent and control the health impacts caused by smoking. Control of health impacts does not only apply to active smokers, but also passive smokers and the surrounding environment exposed to cigarette smoke. The implementation method with the socialization method is an understanding of the condition of smokers in Indonesia and about KTR. In addition, it was conducted with a question and answer session and interviews with health workers and visitors related to cigarettes and KTR. The implementation of this activity is divided into three stages, namely, planning, implementation and evaluation. All health workers and the public understand the dangers of smoking but still carry out smoking activities on the grounds of addiction to cigarettes. KTR socialization provided an understanding that Deli Serdang Regional Hospital had regulations to implement and support KTR issued by the government through the Ministry of Health.

scholarly journals Multitarget Vehicle Tracking and Motion State Estimation Using a Novel Driving Environment Perception System of Intelligent Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yuren Chen ◽  
Xinyi Xie ◽  
Bo Yu ◽  
Yi Li ◽  
Kunhui Lin
Keyword(s):  
State Estimation ◽  
Traffic Safety ◽  
Semantic Segmentation ◽  
Depth Estimation ◽  
Calculation Model ◽  
Intelligent Vehicles ◽  
Vehicle Tracking ◽  
Motion State ◽  
Perception System ◽  
Environment Perception

The multitarget vehicle tracking and motion state estimation are crucial for controlling the host vehicle accurately and preventing collisions. However, current multitarget tracking methods are inconvenient to deal with multivehicle issues due to the dynamically complex driving environment. Driving environment perception systems, as an indispensable component of intelligent vehicles, have the potential to solve this problem from the perspective of image processing. Thus, this study proposes a novel driving environment perception system of intelligent vehicles by using deep learning methods to track multitarget vehicles and estimate their motion states. Firstly, a panoramic segmentation neural network that supports end-to-end training is designed and implemented, which is composed of semantic segmentation and instance segmentation. A depth calculation model of the driving environment is established by adding a depth estimation branch to the feature extraction and fusion module of the panoramic segmentation network. These deep neural networks are trained and tested in the Mapillary Vistas Dataset and the Cityscapes Dataset, and the results showed that these methods performed well with high recognition accuracy. Then, Kalman filtering and Hungarian algorithm are used for the multitarget vehicle tracking and motion state estimation. The effectiveness of this method is tested by a simulation experiment, and results showed that the relative relation (i.e., relative speed and distance) between multiple vehicles can be estimated accurately. The findings of this study can contribute to the development of intelligent vehicles to alert drivers to possible danger, assist drivers’ decision-making, and improve traffic safety.

scholarly journals Flood-fill-based object segmentation and tracking for intelligent vehicles

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141988520
Author(s):  
Phuong Minh Chu ◽  
Seoungjae Cho ◽  
Kaisi Huang ◽  
Kyungeun Cho
Keyword(s):  
Object Segmentation ◽  
Three Dimensional ◽  
Point Clouds ◽  
Intelligent Vehicles ◽  
Segmentation Method ◽  
Second Stage ◽  
Ground Segmentation ◽  
Three Stages ◽  
Flood Fill ◽  
Segmentation And Tracking

In this article, an application for object segmentation and tracking for intelligent vehicles is presented. The proposed object segmentation and tracking method is implemented by combining three stages in each frame. First, based on our previous research on a fast ground segmentation method, the present approach segments three-dimensional point clouds into ground and non-ground points. The ground segmentation is important for clustering each object in subsequent steps. From the non-ground parts, we continue to segment objects using a flood-fill algorithm in the second stage. Finally, object tracking is implemented to determine the same objects over time in the final stage. This stage is performed based on likelihood probability calculated using features of each object. Experimental results demonstrate that the proposed system shows effective, real-time performance.

scholarly journals Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6487
Author(s):  
Xiaobo Chen ◽  
Yanjun Wang ◽  
Ling Chen ◽  
Jianyu Ji
Keyword(s):  
Bayesian Inference ◽  
Target Tracking ◽  
Time Varying ◽  
Variational Bayesian ◽  
Vehicle Communication ◽  
Relative Localization ◽  
Variational Bayesian Inference ◽  
Multiple Vehicles ◽  
Localization Signal ◽  
Cooperative Tracking

Cooperative target tracking by multiple vehicles connected through inter-vehicle communication is a promising way to improve the estimation of target state. The effectiveness of cooperative tracking closely depends on the accuracy of relative localization between host and cooperative vehicles. However, the localization signal usually provided by the satellite-based navigation system is rather susceptible to dynamic driving environment, thus influencing the effectiveness of cooperative tracking. In order to implement reliable cooperative tracking, especially when the statistical characteristic of the relative localization noise is time-varying and uncertain, this paper presents a recursive Bayesian framework which jointly estimates the state of the target and the cooperative vehicle as well as the localization noise parameter. An online variational Bayesian inference algorithm is further developed to achieve efficient recursive estimate. The simulation results verify that our proposed algorithm can effectively boost the accuracy of target tracking when the localization noise dynamically changes over time.

Cooperative Multi-Robot Object Tracking in Unknown Environment Using Covariance Intersection

2013 ◽  
Vol 631-632 ◽  
pp. 1101-1105
Author(s):  
Ming Wu ◽  
Lin Lin Li ◽  
Wei Zhen Hua
Keyword(s):  
Object Tracking ◽  
Point Of View ◽  
The Other ◽  
Fusion Method ◽  
Extend Kalman Filter ◽  
Robot System ◽  
Unknown Environment ◽  
Surrounding Environment ◽  
Covariance Intersection ◽  
Multi Robot

This work presents a approach for multiple cooperating mobile robots for moving object tracking in unknown environment. Each robot in the team uses the full covariance extend Kalman filter based algorithm to simultaneously localize the robot and target while building a landmark feature map of the surrounding environment. Meanwhile, in local robot system the covariance intersection based data fusion method is used to fuse information sent by the other robot teammates, those information may contains the location of target and the location of robot itself from other teammate’s point of view. The method is distributed, and let the multi-robot system have the ability of robustness. The results of simulation validate a higher accuracy of our method compared with non-fusion single robot solution.

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