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Found 5 papers, 1 papers with code
Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles
By incorporating a sparse GP technique in HV modeling and adopting a dynamic GP prediction within the MPC framework, we significantly reduced the computation time of GP-MPC, marking it only 4. 6% higher than that of the conventional MPC.
Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme
Urban Air Mobility, the scenario where hundreds of manned and Unmanned Aircraft System (UAS) carry out a wide variety of missions (e. g. moving humans and goods within the city), is gaining acceptance as a transportation solution of the future.
Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility
With increasing urban population, there is global interest in Urban Air Mobility (UAM), where hundreds of autonomous Unmanned Aircraft Systems (UAS) execute missions in the airspace above cities.
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World
We present a new approach to automated scenario-based testing of the safety of autonomous vehicles, especially those using advanced artificial intelligence-based components, spanning both simulation-based evaluation as well as testing in the real world.
Counterexample-Guided Synthesis of Perception Models and Control
The framework uses a falsifier to find counterexamples, or traces of the systems that violate a safety property, to extract information that enables efficient modeling of the perception modules and errors in it.
