9 citations · 26 across the 11 of their papers we have counts for
12 papers
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction
Justin Lidard, Oswin So, Yanxia Zhang +8
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictor…
Specification-Guided Data Aggregation for Semantically Aware Imitation Learning
Ameesh Shah, Jonathan DeCastro, John Gideon +3
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,…
Learning Latent Traits for Simulated Cooperative Driving Tasks
Jonathan A. DeCastro, Deepak Gopinath, Guy Rosman +3
To construct effective teaming strategies between humans and AI systems in complex, risky situations requires an understanding of individual preferences and behaviors of humans. Pr…
Analyzing Multiagent Interactions in Traffic Scenes via Topological Braids
Christoforos Mavrogiannis, Jonathan DeCastro, Siddhartha S. Srinivasa
We focus on the problem of analyzing multiagent interactions in traffic domains. Understanding the space of behavior of real-world traffic may offer significant advantages for algo…
The Logical Options Framework
Brandon Araki, Xiao Li, Kiran Vodrahalli +3
Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical…
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
Daisuke Nishiyama, Mario Ynocente Castro, Shirou Maruyama +7
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads. In this work, we focus on the…