Spot Locator for Autonomous Parking

2009 ◽  
Author(s):  
Nikolai Moshchuk ◽  
Shih-Ken Chen
Keyword(s):  
Position Estimation ◽  
Ultrasonic Sensors ◽  
Test Vehicle ◽  
Detection Range ◽  
Signal Filtering ◽  
Suggested Algorithm ◽  
Parking Spot ◽  
Vehicle Position ◽  
And Performance ◽  
Left Front

For a semi-autonomous or fully-autonomous parking system, detecting adequate parking spot is the first step. Ultrasonic sensor possesses a good compromise between cost and performance since the detection range is very small. This paper describes a parking assist system with two ultrasonic sensors mounted at the left front and right front corners of the vehicle. Special signal filtering and processing is derived. Kinematic observer for the vehicle position estimation during search and parking phases is discussed. The suggested algorithm is implemented in Matlab/Simulink and was verified in a test vehicle.

scholarly journals Proposal and Performance Evaluation of a Magneto-Striction Type Torque Sensor Consisting of Small-Sized Coils Connected in Series

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Takahiko Tsujisawa ◽  
Kazuhiro Yamakawa
Keyword(s):  
Performance Evaluation ◽  
Response Time ◽  
Detection Method ◽  
Torque Sensor ◽  
Detection Range ◽  
Performance Targets ◽  
Sensor Drift ◽  
In Series ◽  
Target Performance ◽  
And Performance

We propose a sensor consisting of small-sized coils connected in series and a detection method for the sensor based on the iteration of the periodic time difference. The evaluation results are also presented and show the effectiveness of the proposed system. The target performance of the sensor is as follows: (i) a detection range from 0 to ±100 Nm, (ii) a hysteresis error of less than 1%, (iii) an angular-dependent noise of less than 2%, and (iv) a sensor drift of less than 2%. From the evaluation results, it is clear that these performance targets, as well as a sufficient response time, are realized.

Exploring IoT-Enabled Smart Transportation System

2019 ◽  
pp. 186-202 ◽  
Author(s):  
Sushruta Mishra ◽  
Shikha Patel ◽  
Amiya Ranjan Ranjan Panda ◽  
Brojo Kishore Mishra
Keyword(s):  
Weather Conditions ◽  
Transportation System ◽  
Automated System ◽  
Smart Transportation ◽  
Parking Spot ◽  
Parking System ◽  
Traffic Scenario ◽  
Smart Parking ◽  
And Performance ◽  
Real Time Information

Internet of Things (IoT) is a platform that makes a device smart such that every day communication becomes more informative. A Smart Transportation system basically consists of three components which include smart roads, smart vehicles and a smart parking system. Smart roads are used to describe roads that use sensors and IoT technology which makes driving safer and greener. Smart parking system involves an automated system model that can assist the drivers in selecting the suitable parking spot for them. The data that the system collects will be sent for some analysis. It provides real time information to drivers about various aspects of transportation like weather conditions, traffic scenario, road safety, parking space, and many other things. A well-built Smart Transportation system reduces the risk of accidents, improves safety, increases capacity, reduces fuel consumption, and enhances overall comfort and performance for drivers. Our chapter deals with the in-depth discussion of these various aspects of a smart transportation system enabled with IoT technology.

scholarly journals Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4126 ◽  
Author(s):  
Taeklim Kim ◽  
Tae-Hyoung Park
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Reliability Function ◽  
Position Estimation ◽  
Position Tracking ◽  
Radar Sensors ◽  
Vehicle Position ◽  
Sensor Errors ◽  
Fuzzy Adaptive

Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.

Effect of PDOP on performance of Kalman Filters for GNSS-based space vehicle position estimation

GPS Solutions ◽  
2017 ◽  
Vol 21 (3) ◽  
pp. 1379-1387 ◽  
Author(s):  
Sanat K. Biswas ◽  
Li Qiao ◽  
Andrew G. Dempster
Keyword(s):  
Kalman Filters ◽  
Space Vehicle ◽  
Position Estimation ◽  
Vehicle Position

Development and Performance Validation of a Navigation System for an Underwater Vehicle

2016 ◽  
Vol 69 (5) ◽  
pp. 1097-1113 ◽  
Author(s):  
R. Ramesh ◽  
V. Bala Naga Jyothi ◽  
N. Vedachalam ◽  
G.A. Ramadass ◽  
M.A. Atmanand
Keyword(s):  
Shallow Water ◽  
Navigation System ◽  
Estimation Algorithm ◽  
Initial Position ◽  
Position Estimation ◽  
Doppler Velocity ◽  
Unmanned Underwater Vehicles ◽  
Labview Software ◽  
Position Data ◽  
And Performance

Underwater position data is a key requirement for the navigation and control of unmanned underwater vehicles. The proposed navigation scheme can be used in any vessel or boat for any shallow water vehicle. This paper presents the position estimation algorithm developed for shallow water Remotely Operated Vehicles (ROVs) using attitude data and Doppler Velocity Log data with the initial position from the Global Positioning System (GPS). The navigational sensors are identified using the in-house developed simulation tool in MATLAB, based on the requirement of a position accuracy of less than 5%. The navigation system is built using the identified sensors, Kalman filter and navigation algorithm, developed in LabVIEW software. The developed system is tested and validated for position estimation, with an emulator consisting of a GPS-aided fibre optic gyro-based inertial navigation system as a reference, and it is found that the developed navigation system has a position error of less than 5%.

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