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Found 8 papers, 4 papers with code
An active inference model of car following: Advantages and applications
We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models.
Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula
However, this approach produces agents that do not perform robustly in safety-critical settings, an issue that cannot be addressed by simply adding more data to the training set - we show that an agent trained using only a 10% subset of the data performs just as well as an agent trained on the entire dataset.
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
Learning-based methodologies increasingly find applications in safety-critical domains like autonomous driving and medical robotics.
BayesRace: Learning to race autonomously using prior experience
Autonomous race cars require perception, estimation, planning, and control modules which work together asynchronously while driving at the limit of a vehicle's handling capability.
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments.
Efficient Black-box Assessment of Autonomous Vehicle Safety
Using this framework, we conduct the first independent evaluation of a full-stack commercial AV system, Comma AI's OpenPilot.
In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation
Modern treatments for Type 1 diabetes (T1D) use devices known as artificial pancreata (APs), which combine an insulin pump with a continuous glucose monitor (CGM) operating in a closed-loop manner to control blood glucose levels.
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing.
