Abstract
In this paper, we develop new methods to assess safety risks of an integrated GNSS/LiDAR navigation system for highly automated vehicle (HAV) applications. LiDAR navigation requires feature extraction (FE) and data association (DA). In prior work, we established an FE and DA risk prediction algorithm assuming that the set of extracted features matched the set of mapped landmarks. This paper addresses these limiting assumptions by incorporating a Kalman filter innovation-based test to detect unwanted object (UO). UO include unmapped, moving, and wrongly excluded landmarks. An integrity risk bound is derived to account for the risk of not detecting UO. Direct simulations and preliminary testing help quantify the impact on integrity and continuity of UO monitoring in an example GNSS/LiDAR implementation.
Highlights
This paper describes the design, analysis, and preliminary testing of a new method to quantify safety in GNSS/LiDAR navigation systems
Figure 1); the specified standard deviation of the for the vehicle state of interest combination of states); Pχ2 {do f, T } is the probability that a chi-squared-distributed random variable with “dof ” degrees of freedom is lower than some value T; is the tail probability function of the standard normal distribution; nl ml IFE,l is the number of measurements at time step l; is the number of estimated state parameters at time step l; is an integrity risk budget allocation, i.e., a fraction of IREQ,k that we choose to satisfy: IFE,k
R T2 is derived from 0 k χ2τdτ = I minimum detectable error (MDE),l and where, in addition to the variables defined under Equations (1)–(3), we used: is a scalar search parameter that is varied to maximize the integrity risk at each time k; is the worst-case failure mode slope (FMS) over all unwanted objects (UO) hypotheses, determined using the method given in [35]; is the probability that a non-centrally chi-squared distributed random variable with “dof ”
Summary
This paper describes the design, analysis, and preliminary testing of a new method to quantify safety in GNSS/LiDAR navigation systems. This paper builds upon prior work in [1,26,27,28], where we developed an analytical integrity risk prediction method using a multiple-hypothesis innovation-based DA process. We assumed that the set of landmarks in the a-priori map was exactly the same as the one being extracted This assumption was relaxed in [1] where we developed an integrity-risk-minimizing data-selection method. We derived a bound on the risk of incorrect association, with which a subset of measurements can be used while considering potential wrong associations with all landmarks surrounding the LiDAR This bound was used in a preliminary approach to detect UO using solution separation tests.
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