PMP boundary sampling improves data efficiency for learning predictive safety filters via Hamilton-Jacobi reachability, shown in simulations and automotive racing experiments.
Safety-critical model predictive control with discrete-time control barrier function,
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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.
citing papers explorer
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Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle
PMP boundary sampling improves data efficiency for learning predictive safety filters via Hamilton-Jacobi reachability, shown in simulations and automotive racing experiments.
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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.