Proposes CPTO framework combining discrete-time barrier functions and consensus ADMM to achieve safe and consistent real-time trajectory planning for AVs in partially observed dense environments.
Applications of a splitting algorithm to decomposition in convex programming and variational inequalities
2 Pith papers cite this work. Polarity classification is still indexing.
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Empirical comparison finds supervised training yields higher accuracy on convex l1 problems while unsupervised training provides better robustness to distribution shift on nonconvex l0 problems for deep-unfolded ISTA and IHT.
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Safe and Real-Time Consistent Planning for Autonomous Vehicles in Partially Observed Environments via Parallel Consensus Optimization
Proposes CPTO framework combining discrete-time barrier functions and consensus ADMM to achieve safe and consistent real-time trajectory planning for AVs in partially observed dense environments.
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Comparison between Supervised and Unsupervised Learning in Deep Unfolded Sparse Signal Recovery
Empirical comparison finds supervised training yields higher accuracy on convex l1 problems while unsupervised training provides better robustness to distribution shift on nonconvex l0 problems for deep-unfolded ISTA and IHT.