12 citations · 13 across the 9 of their papers we have counts for
10 papers
Spatio-Temporal Retrieval-based Priors for Adaptive Computational Teaching in Driving
Deepak Edakkattil Gopinath, Xiongyi Cui, Jonathan DeCastro +2
Learning-based automated coaching systems for complex motor tasks such as high-performance driving remain limited in the ability to be adaptive by their reliance only on local, con…
Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making
Toshiaki Hori, Jonathan DeCastro, Deepak Gopinath +2
We propose a new approach for solving planning problems with a hierarchical structure, fusing reinforcement learning and MPC planning. Our formulation tightly and elegantly couples…
SimCoachCorpus: A naturalistic dataset with language and trajectories for embodied teaching
Emily Sumner, Deepak E. Gopinath, Laporsha Dees +9
High-quality curated datasets are essential for training and evaluating AI approaches, but are often lacking in embodied interactive domains where language and physical action are…
A Simulator Dataset to Support the Study of Impaired Driving
John Gideon, Kimimasa Tamura, Emily Sumner +7
Despite recent advances in automated driving technology, impaired driving continues to incur a high cost to society. In this paper, we present a driving dataset designed to support…
Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
John Subosits, Jenna Lee, Shawn Manuel +2
Success in racing requires a unique combination of vehicle setup, understanding of the racetrack, and human expertise. Since building and testing many different vehicle configurati…
Computational Teaching for Driving via Multi-Task Imitation Learning
Deepak Gopinath, Xiongyi Cui, Jonathan DeCastro +10
Learning motor skills for sports or performance driving is often done with professional instruction from expert human teachers, whose availability is limited. Our goal is to enable…
Personalizing Driver Safety Interfaces via Driver Cognitive Factors Inference
Emily S Sumner, Jonathan DeCastro, Jean Costa +14
Recent advances in AI and intelligent vehicle technology hold promise to revolutionize mobility and transportation, in the form of advanced driving assistance (ADAS) interfaces. Al…
Autonomous Drifting with 3 Minutes of Data via Learned Tire Models
Franck Djeumou, Jonathan Y. M. Goh, Ufuk Topcu +1
Near the limits of adhesion, the forces generated by a tire are nonlinear and intricately coupled. Efficient and accurate modelling in this region could improve safety, especially…
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…
A hierarchical adaptive nonlinear model predictive control approach for maximizing tire force usage in autonomous vehicles
James Dallas, Michael Thompson, Jonathan Y. M. Goh +1
The ability to reliably maximize tire force usage would improve the safety of autonomous vehicles, especially in challenging edge cases. However, vehicle control near the limits of…