scholarly journals Development of Edge-Node Map Based Navigation System Without Requirement of Prior Sensor Data Collection

2020 ◽  
Vol 32 (6) ◽  
pp. 1112-1120
Author(s):  
Kazuki Takahashi ◽  
◽  
Jumpei Arima ◽  
Toshihiro Hayata ◽  
Yoshitaka Nagai ◽  
...  
Keyword(s):  
Navigation System ◽  
Point Cloud ◽  
Dynamic Environment ◽  
Sensor Data ◽  
Map Matching ◽  
Edge Node ◽  
The Road ◽  
Autonomous Robot Navigation ◽  
On The Road ◽  
Target Environment

In this study, a novel framework for autonomous robot navigation system is proposed. The navigation system uses an edge-node map, which is easily created from electronic maps. Unlike a general self-localization method using an occupancy grid map or a 3D point cloud map, there is no need to run the robot in the target environment in advance to collect sensor data. In this system, the internal sensor is mainly used for self-localization. Assuming that the robot is running on the road, the position of the robot is estimated by associating the robot’s travel trajectory with the edge. In addition, node arrival determination is performed using branch point information obtained from the edge-node map. Because this system does not use map matching, robust self-localization is possible, even in a dynamic environment.

scholarly journals Analysis of the impact of map-matching on the accuracy of propagation models

2007 ◽  
Vol 5 ◽  
pp. 367-372 ◽  
Author(s):  
M. Neuland ◽  
T. Kürner
Keyword(s):  
Communication Networks ◽  
Measurement Data ◽  
Path Loss ◽  
Map Matching ◽  
Sampling Errors ◽  
Propagation Models ◽  
The Road ◽  
On The Road ◽  
Gps Accuracy ◽  
The Impact

Abstract. Propagation models are very important for the development and deployment of wireless communication networks. They are able to predict the path loss for different propagation conditions, but cannot include all propagation phenomena in detail. This fact leads to variations between predicted and measured field strengths. These variations can be reduced by calibrating some parameters of the propagation models with the help of exact measurement data. However, two problems occur when applying measurement data. On the one hand, the maps used for the prediction have only a limited resolution. On the other hand, the GPS data are erroneous due to the limited GPS accuracy and due to sampling errors. These errors can lead to variations up to 200 m between the measured positions and the possible positions on the road network. Therefore, a map-matching algorithm has to be applied which projects the wrong GPS positions automatically onto the street vectors used for the predictions. Thus, a good basis of data for calibration can be created.

SmartEmoDetect: An Internet of Things Based Emotion Monitoring Wearable Technology for Drivers

2019 ◽  
Vol 16 (9) ◽  
pp. 3969-3973
Author(s):  
Jasleen Kaur ◽  
Neera Batra ◽  
Sonali Goyal
Keyword(s):  
Threshold Value ◽  
Emotional Reactions ◽  
Sensor Data ◽  
Stress Monitoring ◽  
The Road ◽  
Severe Accidents ◽  
Warning Message ◽  
Sensor Signals ◽  
The Government ◽  
On The Road

Negative emotional reactions are the major source of severe accidents on the road. In this paper, an IoT based wearable device is proposed that will estimate the four negative emotions (stress, anger, terror, sad) in the driver and hence would be helpful to prevent roadway disasters. An intelligent stress monitoring control system at the cloud to analyze the sensor signals and to make the decision based upon the variation received in the signals is proposed. This system can also be effective for the government bus drivers. An auditory output response is from buzzer and a warning message is displayed on the screen inside the vehicle. The continuously monitored real time sensor data in the form of graphs is displayed on the PC screen considered as a central server. When any of the sensor value exceeds predefined threshold value, the driver is considered to be in subconscious state and the break system will be implemented to stop the DC motor.

scholarly journals Ergonomic Evaluation of Smart Navigation System for Riding on Motorcycle

2018 ◽  
Vol 201 ◽  
pp. 01009
Author(s):  
Po-Hsiang Liu ◽  
Yong-Cheng Li ◽  
Yu-Jung Tsai
Keyword(s):  
Factor Analysis ◽  
Real Time ◽  
Navigation System ◽  
Response Rate ◽  
Smart Device ◽  
The Road ◽  
On The Road ◽  
Real Time Information ◽  
Design Factors ◽  
Time Information

A smart navigation device could be provided to keep riders’ eyes safely on the road while riding on motorcycle. Present study surveyed the riding behaviors and examined the fifteen design factors about navigation device and determined the priority of each factor. Further, navigation device have been evaluated on road. A total of 550 questionnaires were dispatched, with a response rate of 89 % (n = 491). Results of factor analysis showed the six dimensions of prediction information, real-time information, brand and appearance, cost, functionality, efficacy and utility. Further, the highest priority design factors of smart navigation device are reminder for speeding camera, intersection name of next turn, distance of next turn, lane guide and map guide line. After navigation device have been evaluated on road, the smart device that effortlessly guides rider through the safest and most enjoyable riding routes with intuitive light patterns. Results of this study could provide the information for the design of the smart navigation device for riding on motorcycle.

scholarly journals USING DEEP LEARNING TO DIGITIZE ROAD ARROW MARKINGS FROM LIDAR POINT CLOUD DERIVED IMAGES

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 123-129
Author(s):  
M. L. R. Lagahit ◽  
Y. H. Tseng
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Semantic Information ◽  
High Definition ◽  
The Road ◽  
Learning Techniques ◽  
Local Coordinates ◽  
On The Road ◽  
Self Driving Cars

Abstract. The concept of Autonomous vehicles or self-driving cars has recently been gaining a lot of popularity. Because of this, a lot of research is being done to develop the technology. One of which is High Definition (HD) Maps, which are centimeter-level precision 3D maps that contain a lot of geometric and semantic information about the road which can assist the AV when driving. An important component of HD maps is the road markings which indicates a set of rules on how a vehicle should navigate itself on the road. For example, lane lines indicate which part of the road a vehicle can drive on in a certain direction. This research proposes a methodology that uses deep learning techniques to detect road arrows, road markings that show possible driving directions, on LIDAR derived images, and extract them as polyline vector shapefiles. The general workflow consists of (1) converting the LIDAR point cloud to images, (2) training and applying U-Net – a fully convolutional neural network, (3) creating masks from image segmentation results that have been transformed to fit the local coordinates, (4) extracting the polygons and polylines, and finally (5) exporting the vectors in shapefile format. The proposed methodology has shown promising results with object segmentation accuracies comparable with previous related works.

scholarly journals Map Matching for Fixed Sensor Data Based on Utility Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Kangkang He ◽  
Qi Cao ◽  
Gang Ren ◽  
Dawei Li ◽  
Shuichao Zhang
Keyword(s):  
Computation Time ◽  
Sensor Data ◽  
Traffic Information ◽  
Map Matching ◽  
Real World Data ◽  
The Road ◽  
Average Computation Time ◽  
Gps Trajectories ◽  
Traffic Systems ◽  
Almost All

Map matching can provide useful traffic information by aligning the observed trajectories of vehicles with the road network on a digital map. It has an essential role in many advanced intelligent traffic systems (ITSs). Unfortunately, almost all current map-matching approaches were developed for GPS trajectories generated by probe sensors mounted in a few vehicles and cannot deal with the trajectories of massive vehicle samples recorded by fixed sensors, such as camera detectors. In this paper, we propose a novel map-matching model termed Fixed-MM, which is designed specifically for fixed sensor data. Based on two key observations from real-world data, Fixed-MM considers (1) the utility of each path and (2) the travel time constraint to match the trajectories of fixed sensor data to a specific path. Meanwhile, with the laws derived from the distribution of GPS trajectories, a path generation algorithm was developed to search for candidates. The proposed Fixed-MM was examined with field-test data. The experimental results show that Fixed-MM outperforms two types of classical map-matching algorithms regarding accuracy and efficiency when fixed sensor data are used. The proposed Fixed-MM can identify 68.38% of the links correctly, even when the spatial gap between the sensor pair is increased to five kilometers. The average computation time spent by Fixed-MM on one point is only 0.067 s, and we argue that the proposed method can be used online for many real-time ITS applications.

scholarly journals DESIGN OF INDOOR ROBOT PROTOTYPE GUIDED BY RFID BASED POSITIONING AND NAVIGATION SYSTEM

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 175-180
Author(s):  
E. Demiral ◽  
İ. R. Karaş ◽  
Y. Karakaya ◽  
M. Kozlenko
Keyword(s):  
Navigation System ◽  
Road Network ◽  
Target Point ◽  
Shortest Path Algorithm ◽  
The Road ◽  
Work Area ◽  
On The Road ◽  
Target Determination ◽  
Indoor Spaces ◽  
External Intervention

Abstract. In this study, a robot prototype was designed for indoor spaces guided by an RFID-based positioning and navigation system. First, the work area was prepared from cardboard material and RFID cards were placed at predetermined points in the work area. The unique ID number of each RFID card was defined and the coordinates of their location in the work area are known. The RFID reader in the robot prototype reads from less than 5 cm. With a basic approach, when the robot reads an RFID card that it passes over while in motion, the position of the robot is considered the same as the position of the card it is currently reading. The route is defined for the robot prototype whose location is known before starting the movement. When the robot reads a new RFID card during movement, it must move forward or turn left or right to reach the point where the next RFID card is located according to the route. This decision was predetermined and defined according to its location. Alphabot was used as the prototype. Arduino board and additional auxiliary sensors such as gyro sensor, speed sensors, distance sensors are placed on the prototype. The prototype robot is left at any point in the work area and arrives at a target point determined by the user. The required road route to reach the destination is calculated with the shortest path algorithm depending on the road network on the working area and the route is defined. Thus, it is ensured that the prototype reaches the target without any external intervention by the user other than target determination.

Community Colleges: Vital Partners on the Road to Diversity

ASHA Leader ◽  
2006 ◽  
Vol 11 (5) ◽  
pp. 14-17 ◽  
Author(s):  
Shelly S. Chabon ◽  
Ruth E. Cain
Keyword(s):  
Community Colleges ◽  
The Road ◽  
On The Road
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