This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Development of Fault Detection and Emergency Control for Application to Autonomous Vehicle
Technical Paper
2021-01-0075
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Event:
SAE WCX Digital Summit
Language:
English
Abstract
This paper describes a failsafe system of automated driving vehicles. The failsafe system consists of the following two parts: sliding mode observer-based environment sensor, chassis sensor fault detection, and emergency deceleration control. Two sliding mode observers are designed to reconstruct the fault of acceleration and environment sensor(Lidar) in a longitudinal direction. In the environment sensor's fault detection part, the longitudinal vehicle model receives clearance and relative velocity values. Therefore, failure diagnosis is possible regardless of environmental sensors, such as radar, lidar, and camera. This paper's sensor data is the failure of Delphi's Electronically Scanning Radar (ESR) and Ibeo's LUX Lidar installed in an autonomous vehicle. The emergency deceleration control algorithm employs the sliding mode control with adaptive convergence time. In the event of a failure, it is significant to control the vehicle within a short period safely. The Adaptive convergence time concept proves a mathematical convergence of the vehicle control time after a failure occurs. As soon as the error occurred, the error was proved to always converge to zero within the final time. Thus, the proposed method introduces the concept of convergence time, and mathematically demonstrates that the state reached the reference target within the specified time when a failure occurred. In the emergency control part, two processing unit hardware structures are adopted to comply with SAE International standard J3016 and NHTSA autonomous vehicle safety report standards. The proposed fail-safe detection algorithm is evaluated through vehicle test data, and the fail-safe control algorithm evaluates through computer simulation and vehicle tests.
Authors
Topic
Citation
Jong Min, L., Oh, K., and Song, T., "Development of Fault Detection and Emergency Control for Application to Autonomous Vehicle," SAE Technical Paper 2021-01-0075, 2021, https://doi.org/10.4271/2021-01-0075.Also In
References
- U.S. Department of Transportation Automated Driving Systems 2.0, A Vision for Safety NHTSA,. Transp. Syst. 4 3 143 153 Sep 2003
- Netto , M.S. , Chaib , S. , and Mammar , S. Lateral Adaptive Control for Vehicle Lane Keeping Proc. American Control Conference, Boston, MA, USA 4 2693 2698 2004
- Rosen , E. , Kallhammer , J.E. , Eriksson , D. , Nentwich , M. et al. Pedestrian Injury Mitigation by Autonomous Braking Accid Anal Prev 42 6 1949 1957 Nov 2010
- Vorko-Jović , A. , Kern , J. , and Biloglav , Z. Risk Factors in Urban Road Traffic Accidents J. Safety Res 37 1 93 98 Jan 2006
- Amoros , E. , Martin , J.-L. , and Laumon , B. Under-Reporting of Road Crash Casualties in France Accid Anal Prev. 38 4 627 635 2006
- Etemad , A. AdaptIVe: Automated Driving Applications and Technologies for Intelligent Vehicles Automated Driving Springer Cham 2017 535 540
- Alessandrini , A. , Cattivera , A. , Holguin , C. , and Stam , D. CityMobil2: Challenges and Opportunities of Fully Automated Mobility Road Vehicle Automation Springer Cham 2014 169 184
- Akcelik , R. and Basley , M. 2001 Acceleration and Deceleration Models 23trd Conference of Australian Institutes of Transport Research (CAITR 2001)
- Oh , K. , Lee , J. , and Yi , K. Sliding Mode Observer and Control-Based Aproach for Longitudinal Fault-tolerant Control Algorithm of Autonomous Vehicles Trans. Korean Soc. Mech. Eng. B 43 1 31 40 2019 https:/doi.org/10.3795/KSME-B.2019.43.1.031