Search Results for author: Mark Koren

Found 7 papers, 1 papers with code

Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm

1 code implementation27 Jul 2021 Mark Koren, Ahmed Nassar, Mykel J. Kochenderfer

Validating the safety of autonomous systems generally requires the use of high-fidelity simulators that adequately capture the variability of real-world scenarios.

Autonomous Vehicles reinforcement-learning +1

A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems

no code implementations6 May 2020 Anthony Corso, Robert J. Moss, Mark Koren, Ritchie Lee, Mykel J. Kochenderfer

Autonomous cyber-physical systems (CPS) can improve safety and efficiency for safety-critical applications, but require rigorous testing before deployment.

Autonomous Vehicles Collision Avoidance +1

The Adaptive Stress Testing Formulation

no code implementations8 Apr 2020 Mark Koren, Anthony Corso, Mykel J. Kochenderfer

Validation is a key challenge in the search for safe autonomy.

Adaptive Stress Testing without Domain Heuristics using Go-Explore

no code implementations8 Apr 2020 Mark Koren, Mykel J. Kochenderfer

We demonstrate that GE is able to find failures without domain-specific heuristics, such as the distance between the car and the pedestrian, on scenarios that other RL techniques are unable to solve.

Reinforcement Learning (RL)

Efficient Autonomy Validation in Simulation with Adaptive Stress Testing

no code implementations16 Jul 2019 Mark Koren, Mykel Kochenderfer

During the development of autonomous systems such as driverless cars, it is important to characterize the scenarios that are most likely to result in failure.

Adaptive Stress Testing for Autonomous Vehicles

no code implementations5 Feb 2019 Mark Koren, Saud Alsaif, Ritchie Lee, Mykel J. Kochenderfer

This paper presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL) solutions that can scale to large environments.

Autonomous Vehicles Decision Making +2

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