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Found 8 papers, 1 papers with code
Blending Data-Driven Priors in Dynamic Games
As intelligent robots like autonomous vehicles become increasingly deployed in the presence of people, the extent to which these systems should leverage model-based game-theoretic planners versus data-driven policies for safe, interaction-aware motion planning remains an open question.
A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehicles
By leveraging Parametric Weighted Signal Temporal Logic (PWSTL), we formulate the problem of safety-guaranteed preference learning based on pairwise comparisons and propose an approach to solve this learning problem.
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents.
Specification-Guided Data Aggregation for Semantically Aware Imitation Learning
Advancements in simulation and formal methods-guided environment sampling have enabled the rigorous evaluation of machine learning models in a number of safety-critical scenarios, such as autonomous driving.
The Logical Options Framework
Learning composable policies for environments with complex rules and tasks is a challenging problem.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads.
Behaviorally Diverse Traffic Simulation via Reinforcement Learning
Traffic simulators are important tools in autonomous driving development.
Better AI through Logical Scaffolding
We describe the concept of logical scaffolds, which can be used to improve the quality of software that relies on AI components.
