Autonomous systems rely on a perception component to interpret their surroundings, and when misinterpretations occur, they can and have led to serious and fatal system-level failures. Yet, existing methods for testing...
详细信息
ISBN:
(纸本)9781665457019
Autonomous systems rely on a perception component to interpret their surroundings, and when misinterpretations occur, they can and have led to serious and fatal system-level failures. Yet, existing methods for testing perception software remain limited in both their capacity to efficiently generate test data that translates to real-world performance and in their diversity to capture the long tail of rare but safety-critical scenarios. These limitations are particularly evident for perception systems based on LiDAR sensors, which have emerged as a crucial component in modern autonomous systems due to their ability to provide a 3D scan of the world and operate in all lighting conditions. To address these limitations, we introduce a novel approach for testing LiDAR-based perception systems by leveraging existing real-world data as a basis to generate realistic and diverse test cases through mutations that preserve realism invariants while generating inputs rarely found in existing data sets, and automatically crafting oracles that identify potentially safety-critical issues in perception performance. We implemented our approach to assess its ability to identify perception failures, generating over 50,000 test inputs for five state-of-the-art LiDAR-based perception systems. We found that it efficiently generated test cases that yield errors in perception that could result in real consequences if these systems were deployed and does so at a low rate of false positives.
作者：
Fernando, KVUniv Oxford
Wellcome Trust Ctr Human Genet Div Struct Biol Oxford OX3 7BN England
The rank of an eigenvector of an unreduced real symmetric tridiagonal matrix can be determined by just knowing the signs of the elements of the eigenvector and the signs of the off-diagonal entries of the tridiagonal ...
详细信息
The rank of an eigenvector of an unreduced real symmetric tridiagonal matrix can be determined by just knowing the signs of the elements of the eigenvector and the signs of the off-diagonal entries of the tridiagonal matrix. Surprisingly, no arithmetic operations involving real numbers are required to determine this ordinal count. The absence of real arithmetic operations guarantees an error free algorithm. Thus, it is possible to rank and order eigenvectors without knowing the corresponding eigenvalues. It is known that the singular value decomposition (SVD) of bidiagonal matrices are closely related to three tridiagonal eigenvalue problems. Using this connection, it is possible to order singular vectors of bidiagonal matrices without knowing the singular values. Again, no real arithmetic is needed. The ordinal count is a theoretical result, which is valid in exact arithmetic. If eigenvectors and singular vectors are poorly determined in floating-point arithmetic then the ordering procedure can detect faulty eigenpairs. Three standard symmetric eigenproblem routines and two SVD routines from LAPACK are investigated to verify whether the ordinal counts give valid results for computed eigenvectors. For some difficult problems such as the Wilkinson W-n(+) matrix, the ordinal counts disagree with that given by the routines for n greater than or equal to 25. In general, standard LAPACK routines appear to be reasonably resilient even though the routines are not particularly designed to be robust in this context. For some classes of tridiagonals, no failures were detected for n exceeding 3000. However, one of the new LAPACK routines cannot be considered to be robust since it destroys information by thresholding small eigenvector elements to zero. An SVD routine also gave poor results for graded matrices. This could be due to,a bug. Many eigenvalue algorithms do a global sort of the eigenvalues. However, if the ordinal count is reliable then ranking of eigenvectors across
暂无评论