LIDAR Self Driving Car

Increasing population is the major issue of transportation nowadays. People who live and work in the major cities of the world are faced with increasing levels of congestion, delays, total travel time, costs, frustration, accidents and loss of life. The objective of this project is to help prevent traffic accidents and save people’s time by fundamentally changing car use. The system would have sensors to detect the obstacles and to be able to react according to their position. In this project we have developed an automated driving system which drives the car automatically. We have developed a technology for cars that drives it automatically using LIDAR. This car is capable of sensing the surroundings, navigating and fulfilling the human transportation capabilities without any human input. It continuously tracks the surrounding and if any obstacle is detected vehicle senses and moves around and avoids the obstacle. An autonomous car navigation system based on Global Positioning System (GPS) is a new and promising technology, which uses real time geographical data received from several GPS satellites to calculate longitude, latitude, speed and course to help navigate a car. As we know the development of gps is more improved now the accuracy of gps we can see centimetre also so Like for our car to go at specific inputted location we use this gps technology.Lidar is used for sensing the surroundings. Like radar, lidar is an active remote sensing technology but instead of using radio or microwaves it uses electromagnetic waves. Keywords: Congestion, Traffic Accident, LIDAR sensor, Global Positioning System, Electromagnetic waves

Performance Assessment of Real-Time Multiconstellation GNSS PPP Using a Low-Cost Dual-Frequency GNSS Module

The release of low-cost dual-frequency (DF) global navigation satellite system (GNSS) modules provides an opportunity for low-cost precise positioning to support autonomous vehicle applications. The new GNSS modules support the US global positioning system (GPS) L1C/L2C or L5 civilian signals, the Russian GNSS Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS) L1/L2, Europe’s GNSS Galileo E1/E5b, and Chinese GNSS BeiDou B1/B2 signals. The availability of the DF measurements allows for removal of the ionospheric delay, enhancing the obtained positioning accuracy. Unfortunately, however, the L2C signals are only transmitted by modernized GPS satellites. This means that fewer GPS DF measurements are available. This, in turn, might affect the accuracy and the convergence of the GPS-only precise point positioning (PPP) solution. Multi-constellation GNSS PPP has the potential to improve the positioning accuracy and solution convergence due to the high redundancy of GNSS measurements. This paper aims to assess the performance of real-time quad-constellation GNSS PPP using the low-cost u-blox Z9D-F9P module. The assessment is carried out for both open-sky and challenging environment scenarios. Static, simulated-kinematic, and actual field-kinematic trials have been carried out to evaluate real-time PPP performance. Pre-saved real-time precise orbit and clock products from the Centre National d’Etudes Spatiales are used to simulate the real-time scenario. It is shown that the quad-constellation GNSS PPP using the low-cost u-blox Z9D-F9P module achieves decimeter-level positioning accuracy in both the static and simulated-kinematic modes. In addition, the PPP solution convergence is improved compared to the dual- and triple-constellation GNSS PPP counterparts. For the actual kinematic trial, decimeter-level horizontal positioning accuracy is achieved through the GPS + GLONASS + Galileo PPP compared with submeter-level positioning accuracy for the GPS + GLONASS and GPS + Galileo PPP counterparts. Additionally, submeter-level vertical positioning accuracy is achieved through the GPS + GLONASS + Galileo PPP compared with meter-level positioning accuracy for GPS + GLONASS and GPS + Galileo PPP counterparts.

GPS Meteorology : Error in the estimation of precipitable water by ground based GPS system in some meso-scale thunderstorms - A case study

Remote sensing by ground based GPS receivers provide continuous and accurate measurement of precipitable water (PW) of an order of 1.5 mm comparable to radiosondes and water vapour radiometers. In the present work we have examined the amount of PW variation in three thunderstorms accompanied with rain shower that occurred over the GPS station. In all the three thunderstorms event heavy rain was reported. However on comparison of observed rainfall with GPS estimated precipitable water (hourly) in real time, it is observed that among the three, in one event the amount of precipitable water (PW) is much less (~20mm) for the same amount of rainfall. After analysing and taken into account various source of error, we suspect that in a mesoscale thunderstorms or squall lines associated with heavy rainfall, discrepancies arise because the wet mapping functions that used to map the wet delay at any angle to the zenith do not represent the localized atmospheric condition particularly for narrow towering thunder clouds and non-availability of GPS satellites in the zenith direction. On the other hand for the larger thunder cells the atmosphere is very nearly azimuthally symmetric with respect to GPS receiver, the error due to the wet mapping function is minimal.

Modal frequencies of bridges from GNSS (GPS) monitoring data: Experimental, statistical evidence

GNSS technology (known especially for GPS satellites) for measurement of deflections has proved very efficient and useful in bridge structural monitoring, even for short stiff bridges, especially after the advent of 100 Hz GNSS sensors. Mode computation from dynamic deflections has been proposed as one of the applications of this technology. Apart from formal modal analyses with GNSS input, and from spectral analysis of controlled free attenuating oscillations, it has been argued that simple spectra of deflections can define more than one modal frequencies. To test this scenario, we analyzed 21 controlled excitation events from a certain bridge monitoring survey, focusing on lateral and vertical deflections, recorded both by GNSS and an accelerometer. These events contain a transient and a following oscillation, and they are preceded and followed by intervals of quiescence and ambient vibrations. Spectra for each event, for the lateral and the vertical axis of the bridge, and for and each instrument (GNSS, accelerometer) were computed, normalized to their maximum value, and printed one over the other, in order to produce a single composite spectrum for each of the four sets. In these four sets, there was also marked the true value of modal frequency, derived from free attenuating oscillations. It was found that for high SNR (signal-to-noise ratio) deflections, spectral peaks in both acceleration and displacement spectra differ by up to 0.3 Hz from the true value. For low SNR, defections spectra do not match the true frequency, but acceleration spectra provide a low-precision estimate of the true frequency. This is because various excitation effects (traffic, wind etc.) contribute with numerous peaks in a wide range of frequencies. Reliable estimates of modal frequencies can hence be derived from deflections spectra only if excitation frequencies (mostly traffic and wind) can be filtered along with most measurement noise, on the basis of additional data.

Estimation of absolute GNSS satellite antenna group delay variations based on those of absolute receiver antenna group delays

GNSS satellite and receiving antennas exhibit group delay variations (GDV), which affect code pseudorange measurements. Like antenna phase center variations, which affect phase measurements, they are frequency-dependent and vary with the direction of the transmitted and received signal. GNSS code observations contain the combined contributions of satellite and receiver antennas. If absolute GDV are available for the receiver antennas, absolute satellite GDV can be determined. In 2019, an extensive set of absolute receiver antenna GDV was published and, thus, it became feasible to estimate absolute satellite antenna GDV based on terrestrial observations. We used the absolute GDV of four selected receiver antenna types and observation data of globally distributed reference stations that employ these antenna types to determine absolute GDV for the GPS, GLONASS, Galileo, BeiDou, and QZSS satellite antennas. Besides BeiDou-2 satellites whose GDV are known to reach up to 1.5 m peak-to-peak, the GPS satellites show the largest GDV at frequencies L1 and L5 with up to 0.3 and 0.4 m peak-to-peak, respectively. They also show the largest satellite-to-satellite variations within a constellation. The GDV of GLONASS-M satellites reach up to 25 cm at frequency G1; Galileo satellites exhibit the largest GDV at frequency E6 with up to 20 cm; BeiDou-3 satellites show the largest GDV of around 15 cm at frequencies B1-2 and B3. Frequencies L2 of GPS IIIA, E1 of Galileo FOC, and B2a/B2b of BeiDou-3 satellites are the least affected. Their variations are below 10 cm.

Performance Assessment of PPP-AR Positioning and Zenith Total Delay Estimation with Modernized CSRS-PPP

The precise point positioning (PPP) method has become more popular due to powerful online global navigation satellite system (GNSS) data processing services, such as the Canadian Spatial Reference System-PPP (CSRS-PPP). At the end of 2020, the CSRS-PPP service launched the ambiguity resolution (AR) feature for global positioning system (GPS) satellites. More reliable results are obtained with AR compared to the results with traditional ambiguity-float PPP. In this study, the performance of the modernized CSRS-PPP was comparatively assessed in terms of static positioning and zenith total delay (ZTD) estimation. Data for 1 month in the year 2019 obtained from 47 international GNSS service (IGS) stations were processed before and after modernization of the CSRS-PPP. The processes were conducted for GPS and GPS + GLONASS (GLObalnaya NAvigatsionnaya Sputnikovaya Sistema) satellite combinations. Besides, the results were analyzed in terms of accuracy and convergence time. According to the solutions, the AR feature of the CSRS-PPP improved the accuracy by about 50% in the east component for GPS + GLONASS configuration. The root-mean-square error (RMSE) of the ZTD difference between modernized CSRS-PPP service and IGS final troposphere product is 5.8 mm for the GPS-only case.

An analysis of a priori and empirical solar radiation pressure models for GPS satellites

Among the different non-conservative forces acting on GPS satellites, solar radiation pressure (SRP) has the greatest influence and inappropriate modeling of it can introduce an acceleration with the order of 1 × 10−7 m s−2. There are a variety of empirical, analytical, and hybrid empirical-physical models to describe the SRP effect. Among them, the empirical model developed at the Center for Orbit Determination in Europe (CODE) and analytical models based on a box-wing prototype, namely box-shape bus with solar panels, are widely used in the International GNSS Service (IGS) community. To investigate the effects of different a priori SRP models on top of empirical parameterization, two sets of parameters based on the Empirical CODE Orbit Model (ECOM) and two a priori models including the analytical box-wing model and the empirical GPS Solar Pressure Model (GSPM) are tested for the different GPS satellites. Orbit comparison of different SRP scenarios shows that: (1) the two parameterizations of ECOM perform differently for Block IIA and IIR/IIR-M satellites but lead to fewer differences for Block IIF satellites in terms of orbit difference pattern. The 3D RMS of orbit difference of two parameterizations are 25, 30 and 21 mm for each block type. (2) Adoption of a priori model or change of the ECOM parameterization mainly lead to orbit differences varying with both elevation of the Sun w.r.t. the orbit plane and the satellites' argument of latitude w.r.t. the noon point, which is supposed to be related to the special geometry and attitude of every block type. These differences are especially obvious in radial direction. Analysis of estimated parameters of ECOM indicates that (3) the GSPM.04 performs better than box-wing model to describe the main constant solar radiation. It is found (4) that the asymmetry of estimated ECOM parameters in B direction (i.e., the direction completing the orthogonal system with D direction and satellite's solar panel axes), observed for three Block IIR satellites, causes corresponding asymmetrical orbit differences in radial direction when reduced ECOM parameters are used. This does not apply to the extended ECOM parameterization tested in this study, which indicates the insufficiency of reduced ECOM to parameterize asymmetrical satellites.

Characteristics of Onefold Clocks of GPS, Galileo, BeiDou and GLONASS Systems

This research is focused on searching for frequency and noise characteristics for available GNSS (Global Navigation Satellite Systems). The authors illustrated frequency stability and noise characteristics for a selected set of data from four different GNSS systems. For this purpose, 30-s-interval clock corrections were used for the GPS weeks 1982–2034 (the entirety of 2018). Firstly, phase data (raw clock corrections) were preprocessed for shifts and removal of outliers; GLONASS and GPS satellites characterize a smaller number of outliers than BeiDou and Galileo clock products. Secondly, frequency and Hadamard deviation were calculated. This study concludes that the stability of GPS and Galileo is better than that of BDS (BeiDou Navigation Satellite System) and GLONASS. Regarding noise, the GPS, Galileo, and BDS clocks are affected by the random walk modulation noise (RWFM), flashing frequency modulation noise (FFM), and white frequency modulation noise (WFM), whereas the GLONASS clocks are mainly affected only by WFM.

Crisis Mapping and Crowdsourcing in Complex Emergencies

Crisis mappers secure satellite imagery, photos, video, event data, incident data, and other documentary evidence to create an operational picture of a disaster in order to facilitate improved humanitarian response and assistance in a crisis. The era of human-powered crisis mapping between 2009 and 2014 was a bootstrapped effort very much a function of the peculiar state of technological development at the time—available but not yet formalized, streamlined, and automated. Humans filled the gap until machine assistance could catch up. These efforts, often mundane (e.g., cut and paste over and over for hours), were more reflective of the state of technology at the time than anything else. Another precondition that enabled the field to grow is the often taken-for-granted public good provided by the GPS satellites maintained by the U.S. Air Force. Without this service, the project at the time would not have emerged where and when it did. The future will be shaped as a result of improvements in automated forms of data collection; improved machine learning techniques to help filter, identify, visualize, and analyze the data; and the proliferation of low-cost drones and other forms of sensors, to name a few.