A conservative Bayesian framework that incorporates rare failures and prior knowledge is derived, and software reliability growth models are shown to provide useful but non-definitive forecasts of AV disengagement rates.
Planning and decision- making for autonomous vehicles
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Small adversarial perturbations to training data can destabilize data-driven LQR controllers, and the paper proposes DGSM attacks plus regularization and robust control defenses.
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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Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing
A conservative Bayesian framework that incorporates rare failures and prior knowledge is derived, and software reliability growth models are shown to provide useful but non-definitive forecasts of AV disengagement rates.
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Adversarial Destabilization Attacks to Direct Data-Driven Control
Small adversarial perturbations to training data can destabilize data-driven LQR controllers, and the paper proposes DGSM attacks plus regularization and robust control defenses.
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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.