no code implementations • 21 Feb 2024 • Justin Lidard, Haimin Hu, Asher Hancock, Zixu Zhang, Albert Gimó Contreras, Vikash Modi, Jonathan DeCastro, Deepak Gopinath, Guy Rosman, Naomi Leonard, María Santos, Jaime Fernández Fisac
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.
1 code implementation • 30 Oct 2023 • Ruya Karagulle, Nikos Arechiga, Andrew Best, Jonathan DeCastro, Necmiye Ozay
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.
no code implementations • 28 May 2023 • Justin Lidard, Oswin So, Yanxia Zhang, Jonathan DeCastro, Xiongyi Cui, Xin Huang, Yen-Ling Kuo, John Leonard, Avinash Balachandran, Naomi Leonard, Guy Rosman
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents.
no code implementations • 29 Mar 2023 • Ameesh Shah, Jonathan DeCastro, John Gideon, Beyazit Yalcinkaya, Guy Rosman, Sanjit A. Seshia
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.
no code implementations • 24 Feb 2021 • Brandon Araki, Xiao Li, Kiran Vodrahalli, Jonathan DeCastro, Micah J. Fry, Daniela Rus
Learning composable policies for environments with complex rules and tasks is a challenging problem.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 24 Nov 2020 • Daisuke Nishiyama, Mario Ynocente Castro, Shirou Maruyama, Shinya Shiroshita, Karim Hamzaoui, Yi Ouyang, Guy Rosman, Jonathan DeCastro, Kuan-Hui Lee, Adrien Gaidon
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads.
no code implementations • 11 Nov 2020 • Shinya Shiroshita, Shirou Maruyama, Daisuke Nishiyama, Mario Ynocente Castro, Karim Hamzaoui, Guy Rosman, Jonathan DeCastro, Kuan-Hui Lee, Adrien Gaidon
Traffic simulators are important tools in autonomous driving development.
no code implementations • 12 Sep 2019 • Nikos Arechiga, Jonathan DeCastro, Soonho Kong, Karen Leung
We describe the concept of logical scaffolds, which can be used to improve the quality of software that relies on AI components.