Zobrazeno 1 - 10
of 184
pro vyhledávání: '"Williams, Brian C."'
Autor:
Reeves, Marlyse, Williams, Brian C.
Autonomous mobile agents often operate in hazardous environments, necessitating an awareness of safety. These agents can have non-linear, stochastic dynamics that must be considered during planning to guarantee bounded risk. Most state of the art met
Externí odkaz:
http://arxiv.org/abs/2404.07063
Autor:
Zhang, Yuening, Williams, Brian C.
When agents collaborate on a task, it is important that they have some shared mental model of the task routines -- the set of feasible plans towards achieving the goals. However, in reality, situations often arise that such a shared mental model cann
Externí odkaz:
http://arxiv.org/abs/2307.03362
Motion prediction is crucial in enabling safe motion planning for autonomous vehicles in interactive scenarios. It allows the planner to identify potential conflicts with other traffic agents and generate safe plans. Existing motion predictors often
Externí odkaz:
http://arxiv.org/abs/2211.01634
Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for planners in a more scalable and safe way compared to real-world road testing. Existing approaches learn an agent model from large-scale driving data to si
Externí odkaz:
http://arxiv.org/abs/2210.14413
Autor:
Hong, Sungkweon, Williams, Brian C.
Stochastic sequential decision making often requires hierarchical structure in the problem where each high-level action should be further planned with primitive states and actions. In addition, many real-world applications require a plan that satisfi
Externí odkaz:
http://arxiv.org/abs/2205.05228
Autor:
Chen, Jingkai, Li, Jiaoyang, Huang, Yijiang, Garrett, Caelan, Sun, Dawei, Fan, Chuchu, Hofmann, Andreas, Mueller, Caitlin, Koenig, Sven, Williams, Brian C.
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these systems in a manner that ensu
Externí odkaz:
http://arxiv.org/abs/2203.02475
Predicting future motions of road participants is an important task for driving autonomously in urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet it remains an open question to jointly predict scene compli
Externí odkaz:
http://arxiv.org/abs/2202.11884
Autor:
Huang, Xin, Rosman, Guy, Jasour, Ashkan, McGill, Stephen G., Leonard, John J., Williams, Brian C.
When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples. This constraint requires the predictors to generate samples that best support the task given task specification
Externí odkaz:
http://arxiv.org/abs/2110.08750
Autor:
Huang, Xin, Rosman, Guy, Gilitschenski, Igor, Jasour, Ashkan, McGill, Stephen G., Leonard, John J., Williams, Brian C.
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicting continuous trajectories, to improve accuracy and support explainabi
Externí odkaz:
http://arxiv.org/abs/2110.02344
This paper presents fast non-sampling based methods to assess the risk for trajectories of autonomous vehicles when probabilistic predictions of other agents' futures are generated by deep neural networks (DNNs). The presented methods address a wide
Externí odkaz:
http://arxiv.org/abs/2109.09975