A contingency planning method for autonomous vehicles that learns human vehicle uncertainties online and uses reachable set barriers for non-conservative safety.
Interactive multi-modal motion planning with branch model predictive control
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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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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.
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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.