Planetary Exploration 3.0 proposes single adaptive missions that perform both initial exploration and follow-on science on unvisited worlds using software-defined space systems.
Adaptive Stress Testing for Autonomous Vehicles
3 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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abstract
This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the problem as a Markov decision process and use reinforcement learning algorithms to find the most likely failure scenarios. This paper presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL) solutions that can scale to large environments. We show that DRL can find more likely failure scenarios than MCTS with fewer calls to the simulator. A simulation scenario involving a vehicle approaching a crosswalk is used to validate the framework. Our proposed approach is very general and can be easily applied to other scenarios given the appropriate models of the vehicle and the environment.
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2026 3roles
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Replicates SPARK humanoid safety filters and stress-tests them under crowding, noise, and delays, showing trade-offs in goal tracking versus collision reduction.
citing papers explorer
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Planetary Exploration 3.0: A Roadmap for Software-Defined, Radically Adaptive Space Systems
Planetary Exploration 3.0 proposes single adaptive missions that perform both initial exploration and follow-on science on unvisited worlds using software-defined space systems.
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Adversarial Stress Testing of SPARK Humanoid Safety Filters
Replicates SPARK humanoid safety filters and stress-tests them under crowding, noise, and delays, showing trade-offs in goal tracking versus collision reduction.
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