SHIELD derives safe certificates from Lagrangian duality to reduce decision variables and constraints in convex programs, accelerated by a transformer network, delivering order-of-magnitude speedups in stochastic MPC for multi-modal traffic with preserved feasibility and safety.
Predictive control for autonomous driving with uncertain, multimodal predictions
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A contingency planning method for autonomous vehicles that learns human vehicle uncertainties online and uses reachable set barriers for non-conservative safety.
citing papers explorer
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SHIELD: Scalable Optimal Control with Certification using Duality and Convexity
SHIELD derives safe certificates from Lagrangian duality to reduce decision variables and constraints in convex programs, accelerated by a transformer network, delivering order-of-magnitude speedups in stochastic MPC for multi-modal traffic with preserved feasibility and safety.
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Safe and Nonconservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers
A contingency planning method for autonomous vehicles that learns human vehicle uncertainties online and uses reachable set barriers for non-conservative safety.