TY - JOUR
T1 - A High-Integrity and Low-Cost Navigation System for Autonomous Vehicles
AU - Bijjahalli, Suraj
AU - Sabatini, Roberto
N1 - Funding Information:
Manuscript received May 8, 2019; revised October 21, 2019; accepted November 18, 2019. Date of publication December 20, 2019; date of current version December 24, 2020. This work was supported by the Australian Government under the Research Training Program (RTP). The Associate Editor for this article was J. E. Naranjo. (Corresponding author: Roberto Sabatini.) The authors are with the School of Engineering, RMIT University, Melbourne, VIC 3083, Australia (e-mail: [email protected]). Digital Object Identifier 10.1109/TITS.2019.2957876
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - The introduction of autonomous vehicles can potentially lead to enhanced situational awareness and safety for road transport. However, the performance required for autonomous operations places stringent requirements on the vehicle navigation systems design. Relevant performance measures include not only the accuracy of the system but also its ability to detect sensor faults within a specified Time-to-Alert (TTA) and without generating an excessive number of false alarms. A new integrated navigation system architecture is proposed which utilizes Global Navigation Satellite Systems (GNSS), low-cost Inertial Navigation Systems (INS), visual odometry and Vehicle Dynamic Models (VDM). The system design is based on various navigation modes, each with independent failure mechanisms and fault-detection capabilities. A two-step data fusion approach is adopted to optimize the system accuracy and integrity performance. This includes a Knowledge-Based Module (KBM) performing a detailed sensor integrity analysis followed by a conventional Extended Kalman Filer (EKF). CAV navigation integrity requirement (i.e., alert limits and time-to-alert) are considered in the KBM where fault detection probabilities are calculated for each mode and translated to protection levels. A simulation case study is executed to verify the performance of each navigation mode in the presence of faults affecting the individual navigation sensors. Results confirm that the required CAV integrity performances are met, while the inclusion of visual odometry and VDM data provides significant performance enhancements both in terms of accuracy and integrity over existing INS/GNSS systems.
AB - The introduction of autonomous vehicles can potentially lead to enhanced situational awareness and safety for road transport. However, the performance required for autonomous operations places stringent requirements on the vehicle navigation systems design. Relevant performance measures include not only the accuracy of the system but also its ability to detect sensor faults within a specified Time-to-Alert (TTA) and without generating an excessive number of false alarms. A new integrated navigation system architecture is proposed which utilizes Global Navigation Satellite Systems (GNSS), low-cost Inertial Navigation Systems (INS), visual odometry and Vehicle Dynamic Models (VDM). The system design is based on various navigation modes, each with independent failure mechanisms and fault-detection capabilities. A two-step data fusion approach is adopted to optimize the system accuracy and integrity performance. This includes a Knowledge-Based Module (KBM) performing a detailed sensor integrity analysis followed by a conventional Extended Kalman Filer (EKF). CAV navigation integrity requirement (i.e., alert limits and time-to-alert) are considered in the KBM where fault detection probabilities are calculated for each mode and translated to protection levels. A simulation case study is executed to verify the performance of each navigation mode in the presence of faults affecting the individual navigation sensors. Results confirm that the required CAV integrity performances are met, while the inclusion of visual odometry and VDM data provides significant performance enhancements both in terms of accuracy and integrity over existing INS/GNSS systems.
KW - Connected autonomous vehicles
KW - GNSS
KW - integrity augmentation
KW - intelligent transport systems
KW - Kalman filter
KW - navigation system
KW - sensor fusion
UR - http://www.scopus.com/inward/record.url?scp=85076830061&partnerID=8YFLogxK
U2 - 10.1109/TITS.2019.2957876
DO - 10.1109/TITS.2019.2957876
M3 - Article
AN - SCOPUS:85076830061
SN - 1524-9050
VL - 22
SP - 356
EP - 369
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 1
M1 - 8937757
ER -
