Abstract
In this paper, a vehicle localization fail safe process is proposed for improving localization reliability for level 3 autonomous driving. This process does not necessitate additional and expensive sensor configuration using sensor fusion with practical usage and high density maps for localization fail safe. The proposed process also suggests a three-step safety mechanism. The first step is to detect and monitor in-vehicle sensors. The second step constitutes Dead Reckoning (DR) model-based fail monitoring. The final is a map-matching fail safe to detect and to identify fail level and this algorithm recovers abnormal positions of map-matching results. The fail detection algorithm and monitoring logic and thresholds were validated and identified by vehicle endurance run tests comprising over 100,000 km of driving. The performance of DR fail monitoring and map-matching base fail logic were evaluated by vehicle-simulations on sensor measurements. The results demonstrated that the proposed process achieves improvement of reliability with accuracy fault detection and identification for abnormal cases on the fail level.
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Abbreviations
- ω k :
-
speed at each wheel, km/h
- V ch :
-
characteristic speed, m/s
- Ψ est :
-
estimated yaw rate, rad/s
- Ψ raw :
-
yaw rate measurement, rad/s
- θ :
-
steering wheel angle, deg
- θ ratio :
-
steering wheel angle, ratio
- l wheel :
-
wheel length, m
- V x :
-
measured wheel speed, km/h
- σ yrs :
-
yaw rate noise, rad/s
- M yaw :
-
margin of yaw rate
- M spd :
-
margin of wheel speed
- T w :
-
time window, ms
- TDE DR :
-
travel distance error of DR, %
- P reg :
-
required localization accuracy, m
References
Basarke, C., Berger, C. and Rumpe, B. (2007). Software & systems engineering process and tools for the development of autonomous driving intelligence. J. Aerospace Computing, Information, and Communication 4, 12, 1158–1174.
Bertozzi, M., Broggi, A., Coati, A. and Fedriga, R. I. (2013). A 13,000 km intercontinental trip with driverless vehicles: The VIAC experiment. IEEE Intelligent Transportation Systems Magazine 5, 1, 28–41.
Bishop, R. (2000). A survey of intelligent vehicle applications worldwide. Proc. IEEE Intelligent Vehicles Symp. 2000 (Cat. No. 00TH8511), Dearborn, MI, USA.
Chatham, A. (2013). Google’s Self Driving Cars: The Technology, Capabilities, Challenges. Embedded Linux Conf., San Francisco, CA, USA.
Eskandarian, A. (Ed.). (2012). Handbook of intelligent vehicles (Vol. 2). Springer-Verlag. London, UK.
Hörwick, M. and Siedersberger, K. H. (2010). Strategy and architecture of a safety concept for fully automatic and autonomous driving assistance systems. 2010 IEEE Intelligent Vehicles Symp., San Diego, CA, USA.
ISO (2011). ISO 26262: 2011 Road vehicles Functional safety. ISO 26262:2011 Roadvehicles - Functional safety (ISO 26262). Geneva, Switzerland.
Jeong, Y. H. and Lee, K. J. (2015). Virtual sensor based vehicle sensor fault tolerant algorithm. KSME, 1761–1766.
Jo, K., Chu, K. and Sunwoo, M. (2013). GPS-bias correction for precise localization of autonomous vehicles. 2013 IEEE Intelligent Vehicles Symp. (IV), Gold Coast City, Australia.
Koopman, P. and Wagner, M. (2016). Challenges in autonomous vehicle testing and validation. SAE Int. J. Transportation Safety 4, 1, 15–24.
Lee, B. H., Song, J. H., Im, J. H., Im, S. H., Heo, M. B. and Jee, G. I. (2015). GPS/DR error estimation for autonomous vehicle localization. Sensors 15, 8, 20779–20798.
Levinson, J. and Thrun, S. (2010). Robust vehicle localization in urban environments using probabilistic maps. 2010 IEEE Int. Conf. Robotics and Automation, Anchorage, AK, USA.
Nah, J. W., Kim, W. G., Yi, K.-S. and Lee, J. (2009). Fail-safe Control Algorithm for an Autonomous Vehicle, KSAE 2009 Annual Conf. Exhibition, 1929–1935.
Sari, B. and Reuss, H. C. (2018). Fail-operational safety architecture for ADAS systems considering domain ECUs. SAE Technical Paper No. 2018-01-1069.
Schopper, M., Henle, L. and Wohland, T. (2013). Intelligent Drive Vernetzte Intelligenz für mehr Sicherheit. ATZextra 18, 5, 106–114.
Skog, I. and Handel, P. (2009). In-car positioning and navigation technologies—A survey. IEEE Trans. Intelligent Transportation Systems 10, 1, 4–21.
Suhr, J. K., Jang, J., Min, D., and Jung, H. G. (2016). Sensor fusion-based low-cost vehicle localization system for complex urban environments. IEEE Trans. Intelligent Transportation Systems 18, 5, 1078–1086.
Wan, G., Yang, X., Cai, R., Li, H., Zhou, Y., Wang, H. and Song, S. (2018). Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes. 2018 IEEE Int. Conf. Robotics and Automation (ICRA), Brisbane, Australia.
Wikipedia (2009). List of self-driving car fatalities. http://en.wikipedia.org/wiki/List_of_self-driving_car_fatalities
Yoon, B. J., Lee, J. Y., Kim, J. H. and Han, C. S. (2011). Development of a navigation algorithm with dead reckoning for unmanned ground vehicles. Int. J. Automotive Technology 12, 1, 111–118.
Acknowledgement
This work was supported by the Technology Innovation Program (No.0420-20190073, Development and Evaluation of Automated Driving Systems for Motorway and City Road) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). This research was supported (in part) by SNU-IAMD.
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Seo, K., Lee, J., Lee, Jy. et al. Fail Safe Process of Vehicle Localization for Reliability Improvement of LV3 Autonomous Driving. Int.J Automot. Technol. 22, 529–535 (2021). https://doi.org/10.1007/s12239-021-0049-8
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DOI: https://doi.org/10.1007/s12239-021-0049-8