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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.
Bijjahalli et al. (Tue,) studied this question.