GPS World, May 2017

CONNECTED CAR This is vital for this map building method of navigation This section deals only with sensors that produce continuous output The next section discusses discrete features Cross Correlation Coefficients The correlation coefficient see online version of this article for derivation equations is used to calculate the cross correlation of two rounds of sensor data The cross correalation coefficient is a normalized value If a signal is correlated with itself at zero offset autocorrelation this would give a value of 1 entirely uncorrelated data gives 0 Signals 180 out of phase would give a correlation value of 1 The cross correlation coefficients are shown in TABLE 2 for all of the sensors It shows the coefficients for the four different road types using combinations of rounds round 1 and 2 round 2 and 3 and round 1 and 3 for each three days from the same days and the average of the coefficients for all the combinations The sensors with higher coefficients are discussed in more detail in the following subsections Road signs do not have cross correlation coefficients they are treated differently as this is a discrete measurement Accelerometer The magnitude of the acceleration from a accelerometer triad was used in this experiment as a method of measuring road texture A zoomed in section of the acceleration as recorded from the accelerometer against the distance travelled can be seen in FIGURE 4 It is difficult to see similarities in the output from the different rounds although the accelerometer can show movement from stationary to driving and this was used to initialize the sensor outputs from the XSens IMU This is shown in FIGURE 5 at 44s Barometer The barometer measures height change of the vehicle This sensor consistently produced the highest cross correlation coefficient shown in FIGURE 6 TABLE 2 Cross correlation coefficients for sensor outputs for the four road types Magnetometer The magnetometer produced data with distinct spikes caused by various magnetic anomalies in the environment being travelled through This can be seen in FIGURE 7 for the high speed road FIGURE 8 is a zoomed in section of the magnetometer data from the high speed road in Figure 7 It shows correlation with an offset of approximately 44m between round 1 and round 3 This is mostly due to synchronization errors between the magnetometer counter and the GNSS receiver clock This is the reason a second run of the road experiment was completed 24 GPS WORLD WWW GPSWORLD COM MAY 2017 Microphone The microphone was able to pick up clear signals when the vehicle was stationary and the signal seems to be dependent on the speed of the vehicle FIGURE 9 shows the profile from the microphone It may be possible to combine this data with the accelerometer or odometer data to develop a clearer picture of what sound is resulting directly from the road surface and what is speed related although this still may not result in a useful feature for this study Thermometer Temperature can vary particularly in a rural environment seen in FIGURE 10 Similarities are not FIGURE 6 Comparison of height profile over 3 days with minimums set to zero FIGURE 7 Vertical axis magnetic field profile for a high speed road Sensor type Cross Correlation Coefficient High Speed Rural Urban Suburban Accelerometer 000 001 003 001 Air Quality 033 026 009 016 Barometer 098 099 085 091 Dust 005 003 000 007 Ambient Light 031 059 031 077 Magnetometer 038 084 062 026 Microphone 053 059 039 017 Thermometer 029 052 010 005 FIGURE 4 Profile from accelerometer attached to axle FIGURE 5 Accelerometer data showing vehicle setting off