Introduces an ADMM-based distributed trajectory negotiation framework with interactive attention for robust real-time CAV coordination under uncertainty, reporting up to 40.79% collision reduction and 15.4% lower computation in simulations plus real-world validation.
Distributed optimization and statistical learning via the alternating direction method of multipliers
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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.
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Robust Real-Time Coordination of CAVs: A Distributed Optimization Framework under Uncertainty
Introduces an ADMM-based distributed trajectory negotiation framework with interactive attention for robust real-time CAV coordination under uncertainty, reporting up to 40.79% collision reduction and 15.4% lower computation in simulations plus real-world validation.
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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.