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Found 7 papers, 2 papers with code
GENESIS-RL: GEnerating Natural Edge-cases with Systematic Integration of Safety considerations and Reinforcement Learning
In the rapidly evolving field of autonomous systems, the safety and reliability of the system components are fundamental requirements.
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems.
Temporal Shift Reinforcement Learning
The function approximators employed by traditional image-based Deep Reinforcement Learning (DRL) algorithms usually lack a temporal learning component and instead focus on learning the spatial component.
The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior
First, we found that these rules were enough for these models to achieve a high classification accuracy on the dataset.
Interpretable UAV Collision Avoidance using Deep Reinforcement Learning
The significant components of any successful autonomous flight system are task completion and collision avoidance.
Leveraging Classification Metrics for Quantitative System-Level Analysis with Temporal Logic Specifications
In many autonomy applications, performance of perception algorithms is important for effective planning and control.
Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks
We define a "rulebook" as a pre-ordered set of "rules", each akin to a violation metric on the possible outcomes ("realizations").
