GPS World, May 2017

CONNECTED CAR The following subsections will describe a number of ways of combining the scores before estimating the position It will be noted if these techniques could also be used to combine position estimates Equal Weighting A simpl e combination technique is for each feature score to have equal weighting The equal weighting used earlier took the two scores and found the average This way no single feature will dominate the navigation solution As the feature scores are not probabilities the values are not self weighting therefore it cannot be presumed that that equal weighting would always provide an optimal position estimation Test Data Weighting This method takes a set of experimental data and empirically determines the weighting coefficients based on the best position solution in this test dataset The test data would be used to maximize the score of the combined features using weighting at the correct position This would have the benefit of using real data to determine the weighting but its strength is based on how representative the test dataset is to the environments that the car will travel in Environment Weighting This would detect the environmental context and use this to select an appropriate set of weights For example the presence of many Wi Fi sources would suggest a suburban or urban environment while a vehicle speed of 31m s 70 mph 113 km h would suggest that the vehicle is likely to be on a highway Based on this knowledge it would possible to use a specific weighting coefficient set is developed for that environmental context Cross Correlation Weighting This weights each feature according to the characteristics of the cross correlation coefficients profile obtained using the scanning method described earlier This enables the weighing to adapt to the quality of the data FIGURE 15 shows traits of a set of peaks that affect the confidence in the highest peak being the correct position Taking the uncertainty in the current position only peaks that for example fall within 3 standard deviations would be evaluated The characteristics of the tallest peaks compared with the others would be used to determine a measure of confidence for that feature There will be more confidence in the tallest peak h0 if there is a greater difference between its height and that of the other peaks within the uncertainty range h1 2 3 In TABLE 3 this is height The next factor is the number of peaks within the uncertainty range No Peaks The more peaks the less confidence that the correct peak has been chosen as the position estimate The average cross correlation coefficient within the uncertainty region γ would affect the confidence in the estimated position If the average coefficient value Av CC was similar to that of the highest peak this suggests insufficient variation in the data being analyzed from that feature Finallym the standard deviation could be used Calculating how many standard deviation Std Dev the highest peak was from the mean could provide a weighting value Each of these characteristics was looked at separately and compared against the benchmark of equal weighting using the scanning method comparing multiple pairs of rounds on different routes It can be seen in Table 3 that the standard deviation from the mean provided the best weighting outcomes To optimize the weighting algorithm it may be that using a combination of these profile characteristics would provide the best position estimation FIGURE 16 and FIGURE 17 seen in online version of this article show examples of cross correlation profiles they show high and low confidence respectively Figure 16 is the cross correlation of data from day three rounds two and 28 GPS WORLD WWW GPSWORLD COM MAY 2017 three on suburban roads It has a few spaced out peaks over the full profile and one of the peaks is clearly higher than the others Figure 17 is the cross correlation of data from day two round three and day three round three from the high speed road It has many similar height peaks all around the value of 05 CONCLUSION Environmental features have sufficient variability spatially and stability temporally for a database of features to be developed to create a map of the environment This supports the hypothesis that it is feasible to map a space and then create a featuremapping and navigation algorithm using a combination of environmental feature sensors a GNSS receiver and sensors for dead reckoning FUTURE WORK The next step of the project is to develop a feature matching mapping and navigation algorithm that incorporates inputs from the multiple sensors a GNSS receiver map matching and sensors for dead reckoning The algorithm will run collecting sensor data while GNSS receiver data is available and store this in a database along with location stamps until called upon in times of GNSS receiver signal disturbance The data from the road experiments will be used for a test database in developing the navigation system ACKNOWLEDGMENTS Debbie Walter is funded by Engineering and Physical Sciences Research Council EPSRC and Terrafix ltd The authors thank Paul Neesham for a method of manually recording street signs seen in video footage and Juliusz Romaniuk of Terrafix for advice and creating hardware that contained the sensors carrier frequencies Author bios online