Road Environment Recognition Using On-vehicle LIDAR

2006 ◽  
Author(s):  
K. Takagi ◽  
K. Morikawa ◽  
T. Ogawa ◽  
M. Saburi
Keyword(s):  
Environment Recognition

scholarly journals Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 83
Author(s):  
Keiichi Zempo ◽  
Taiga Arai ◽  
Takuya Aoki ◽  
Yukihiko Okada
Keyword(s):  
Radio Wave ◽  
Indoor Positioning ◽  
Measurement Range ◽  
Pulse Interval ◽  
Retail Stores ◽  
Positioning System ◽  
Radio Waves ◽  
Positioning Systems ◽  
Environment Recognition ◽  
Indoor Positioning System

To evaluate and improve the value of a service, it is important to measure not only the outcomes, but also the process of the service. Value co-creation (VCC) is not limited to outcomes, especially in interpersonal services based on interactions between actors. In this paper, a sensing framework for a VCC process in retail stores is proposed by improving an environment recognition based indoor positioning system with high positioning performance in a metal shelf environment. The conventional indoor positioning systems use radio waves; therefore, errors are caused by reflection, absorption, and interference from metal shelves. An improvement in positioning performance was achieved in the proposed method by using an IR (infrared) slit and IR light, which avoids such errors. The system was designed to recognize many and unspecified people based on the environment recognition method that the receivers had installed, in the service environment. In addition, sensor networking was also conducted by adding a function to transmit payload and identification simultaneously to the beacons that were attached to positioning objects. The effectiveness of the proposed method was verified by installing it not only in an experimental environment with ideal conditions, but posteriorly, the system was tested in real conditions, in a retail store. In our experimental setup, in a comparison with equal element numbers, positioning identification was possible within an error of 96.2 mm in a static environment in contrast to the radio wave based method where an average positioning error of approximately 648 mm was measured using the radio wave based method (Bluetooth low-energy fingerprinting technique). Moreover, when multiple beacons were used simultaneously in our system within the measurement range of one receiver, the appropriate setting of the pulse interval and jitter rate was implemented by simulation. Additionally, it was confirmed that, in a real scenario, it is possible to measure the changes in movement and positional relationships between people. This result shows the feasibility of measuring and evaluating the VCC process in retail stores, although it was difficult to measure the interaction between actors.

Urban environment recognition based on theGNSSsignal characteristics

Navigation ◽  
2019 ◽  
Vol 66 (1) ◽  
pp. 211-225 ◽  
Author(s):  
Yuze Wang ◽  
Peilin Liu ◽  
Qiang Liu ◽  
Muhammad Adeel ◽  
Jiuchao Qian ◽  
...  
Keyword(s):  
Urban Environment ◽  
Environment Recognition

Vision based environment recognition for mobile robot in irregular ground

2007 ◽  
Author(s):  
Shin'ya Okazaki ◽  
Takayuki Tanaka ◽  
Syun'ichi Kaneko ◽  
Akihiko Matsushita
Keyword(s):  
Mobile Robot ◽  
Environment Recognition

scholarly journals Intelligent Highway Traffic Detection Algorithm based on Deep Learning

2021 ◽  
Author(s):  
ming ji ◽  
Chuanxia Sun ◽  
Yinglei Hu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Traffic Congestion ◽  
Road Network ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Traffic Data ◽  
Vehicle Recognition ◽  
Environment Recognition

Abstract In order to solve the increasingly serious traffic congestion problem, an intelligent transportation system is widely used in dynamic traffic management, which effectively alleviates traffic congestion and improves road traffic efficiency. With the continuous development of traffic data acquisition technology, it is possible to obtain real-time traffic data in the road network in time. A large amount of traffic information provides a data guarantee for the analysis and prediction of road network traffic state. Based on the deep learning framework, this paper studies the vehicle recognition algorithm and road environment discrimination algorithm, which greatly improves the accuracy of highway vehicle recognition. Collect highway video surveillance images in different environments, establish a complete original database, build a deep learning model of environment discrimination, and train the classification model to realize real-time environment recognition of highway, as the basic condition of vehicle recognition and traffic event discrimination, and provide basic information for vehicle detection model selection. To improve the accuracy of road vehicle detection, the vehicle target labeling and sample preprocessing of different environment samples are carried out. On this basis, the vehicle recognition algorithm is studied, and the vehicle detection algorithm based on weather environment recognition and fast RCNN model is proposed. Then, the performance of the vehicle detection algorithm described in this paper is verified by comparing the detection accuracy differences between different environment dataset models and overall dataset models, different network structures and deep learning methods, and other methods.

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