Inside GNSS Media & Research

NOV-DEC 2017

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Page 45 of 67

46 Inside GNSS N O V E M B E R / D E C E M B E R 2 0 1 7 Association Filter," provides a detailed derivation of the probability of correct association given measurements. How- ever, this Bayesian approach is not well suited for safety-critical applications due to the lack of risk prediction capa- bility, and to the problem of bounding the a-posteriori probability of associa- tion (a similar issue is encountered in the paper by F.C. Chan, et alia]). Anoth- er insightful approach is followed in the paper by J. Areta, et alia]. However, it ma kes approx imations t hat do not necessarily upper-bound risks, hence do not guarantee safe operation, and it presents exact solutions that can only be evaluated using computationally expen- sive numerical methods, not adequate for real-time navigation. Also, the risk of FE is not addressed. In response, we have been developing a new, computationally-efficient integ- rity risk prediction method to ensure safety of localization using LiDAR-based FE and DA. We have derived a multi- ple-hypothesis innovation-based DA method that provides the means to pre- dict the probability of incorrect associa- tions considering all potential landmark permutations. (For more details on these methods, see the following four papers in Additional Resources, Nos. 31, 49, 50 and 51.) We also determined a probabilistic lower bound on the minimum feature separation, which is guaranteed at FE, with pre-defined integrity risk allo- cation. The separation bound can be incorporated in an overall integrity risk equation. This new method was ana- lyzed and tested to quantify the impact of incorrect associations on integrity risk. It showed that the positioning error covariance can be a misleading safety performance metric since cases were found where the contributions of incorrect associations to integrity risk far surpassed that of nominal errors accounted for in the positioning error covariance. In addition, the following key safety-tradeoff was illustrated: the more measurements are extracted, the lower the integrity risk contribution is under the correct association hypoth- esis, but the higher the other integrity risk contributions become because the risk of incorrect associations increases in the presence of cluttered, poorly-distin- guishable landmarks. Finally, being sur- rounded by many landmarks increases the probability of continuous, uninter- rupted navigation. e next step of this research aims at dealing with unmapped and non-static obstacles, and at quanti- fying the continuity risk of FE and DA. Conclusion Looking at the emergence of future HAV technology with the prior experience of aircra navigation safety provides the means to scale up the challenges that lie ahead in the development of fully autonomous (Level 4 and 5) driverless cars. Many parallels can already be drawn between aviation safety require- ments and early HAV standards and regulations. Still, the methods to fulfill these standards and regulations have to be established. If analytical methods are pursued, the following tasks need to be accomplished: (1) establish high-integ- rity raw sensor measurement error and fault models for non-GPS sensors; (2) develop analytical methods to quantify the safety risk of feature extraction and data association algorithms required in LiDAR, radar, and other pre-processing steps in camera-based localization; (3) design multi-sensor pose estimators and integrity monitors to evaluate the impact of undetected sensor faults on safety risk; and (4) derive, analyze, and experimentally implement integrity risk prediction in dynamic environments. If these challenges are overcome, one will be able to quantify and prove the performance of an HAV's naviga- tion system — an essential part of safety. Proving navigation system integrity will also help give humans more confidence to trust HAVs, thus further develop- ing the symbiotic relationship between humans and co-robots. Finally, as HAV technology progresses from driver's aids such as active brake assist to full autono- mous driving, this research is relevant now and will remain essential through- out the evolution of HAV technology. Additional Resources [1] Abuhashim, T.S., M.F. AbdelHafez, and M.-A. AlJarrah. Building a robust integrity monitoring algorithm for a low cost gps-aided-ins system. International Journal of Control, Automation, and Systems, 8(5):11081122, 2010. [2] Ackerman , E., "Self-Driving Cars Were Just Around the Corner—in 1960", IEEE Spectrum, September 2016 [3] Ackerman, E., "After Mastering Singapore's Streets, NuTonomy's Robo-taxis Are Poised to Take on New Cities," IEEE Spectrum, 2016. [4] Areta, J., Y. Bar-Shalom, and R. Rothrock, "Misasso- ciation Probability in M2TA and T2TA," J. of Advances in Information Fusion, Vol. 2, No. 2, 2007, pp. 113-127. [5] Bailey, T., Mobile Robot Localization and Mapping in Extensive Outdoor Environments. PhD thesis, The University of Sydney, 2002. [6] Bailey, T., and J. Nieto. Scan-slam: Recursive mapping and localization with arbitrary-shaped landmarks. In Workshop at the Institute of Electrical and Electronics Engineers Robotics Science and Systems (IEEE RSS), 2008. [7] Bakhache, B., A Sequential RAIM Based on the Civil Aviation Requirements. In Proceedings of the 12th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 1999), pages 1201–1210, 1999. [8] Basnayake, C., M. Joerger, and J. Aulde, "Safety-Crit- ical Positioning for Automotive Applications", Inside GNSS Webinar, 2016. [9] Bar-Shalom, Y., F. Daum, and J. Huang, "The Proba- bilistic Data Association Filter," IEEE Control Systems Magazine, 2009, pp. 82-100. [10] Bar-Shalom, Y., and T. E. Fortmann. Mathematics in Science and Engineering, chapter Tracking and Data Association. Academic Press, 1988. [11] Bengtsson, O., and A.J. 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Pervan, "Bayesian Fault-Tolerant Position Estimator and Integ- rity Risk Bound for GNSS Navigation," Journal of Navi- gation of the RIN, available on CJO2014, doi:10.1017/ S0373463314000241, 2014. [17] Chow, E., and A. Willsky. Analytical redundancy and the design of robust failure detection systems. IEEE Transactions on Automatic Control, 29(7):603614, 1984. [18] Choukroun, D., and J. Speyer. Mode estimation via conditionally linear filtering: Application to gyro fail- ure monitoring. AIAA Journal of Guidance, Control, and Dynamics, 65(2):632644, 2012. [19] Clot, A., C. Macabiau, I. Nikiforov, and B. Roturier. Sequential RAIM Designed to Detect Combined Step Ramp Pseudo-Range Error. In Proceedings of the 19th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2006), page 26212633, 2006. [20] ooper, A.J., A Comparison of Data Association Tech- niques for Simultaneous Localization and Mapping. PhD thesis, Massachusetts Institute of Technology, 2005. [21] DARPA, " The Six Finishers of the DARPA Urban Challenge," available online at http://archive.darpa. mil/grandchallenge/index.html, 2007. [22] Defense Advanced Research Projects Agency (DARPA), "Robots conquer DARPA Grand Challenge," Press Release, U.S. Department of Defense (DoD), 2005. [23] Department of Transportation (DOT ) National Highway Traffic Safety Administration (NHTSA) "Fed- eral Automated Vehicles Policy: Accelerating the Next Revolution In Roadway Safety," 2016 [24] Diesel, J., and S. Luu. GPS/IRS AIME: Calculation of Thresholds and Protection Radius Using Chi-Square Methods. In Proceedings of the 8th International Tech- nical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 1995), page 19591964, 1995. HAV SAFET Y

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