Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Chakraborty, Neeloy"'
Real-time Advisory (RTA) systems, such as navigational and eco-driving assistants, are becoming increasingly ubiquitous in vehicles due to their benefits for users and society. Until autonomous vehicles mature, such advisory systems will continue to
Externí odkaz:
http://arxiv.org/abs/2407.13775
Autor:
Hasan, Aamir, Chakraborty, Neeloy, Chen, Haonan, Cho, Jung-Hoon, Wu, Cathy, Driggs-Campbell, Katherine
Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these approaches are limited in practice as they assume precise control over
Externí odkaz:
http://arxiv.org/abs/2407.00553
We investigate the feasibility of deploying reinforcement learning (RL) policies for constrained crowd navigation using a low-fidelity simulator. We introduce a representation of the dynamic environment, separating human and obstacle representations.
Externí odkaz:
http://arxiv.org/abs/2405.16830
Autonomous systems are soon to be ubiquitous, from manufacturing autonomy to agricultural field robots, and from health care assistants to the entertainment industry. The majority of these systems are developed with modular sub-components for decisio
Externí odkaz:
http://arxiv.org/abs/2403.16527
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
Autor:
Hasan, Aamir, Chakraborty, Neeloy, Chen, Haonan, Cho, Jung-Hoon, Wu, Cathy, Driggs-Campbell, Katherine
Intelligent driving systems can be used to mitigate congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these systems assume precise control over autonomous vehicle fleets, and are
Externí odkaz:
http://arxiv.org/abs/2308.00864
The effects of traffic congestion are widespread and are an impedance to everyday life. Piecewise constant driving policies have shown promise in helping mitigate traffic congestion in simulation environments. However, no works currently test these p
Externí odkaz:
http://arxiv.org/abs/2302.09140
Autor:
Chang, Peixin, Liu, Shuijing, Ji, Tianchen, Chakraborty, Neeloy, Hong, Kaiwen, Driggs-Campbell, Katherine
A command-following robot that serves people in everyday life must continually improve itself in deployment domains with minimal help from its end users, instead of engineers. Previous methods are either difficult to continuously improve after the de
Externí odkaz:
http://arxiv.org/abs/2301.09749
Autor:
Chakraborty, Neeloy, Hasan, Aamir, Liu, Shuijing, Ji, Tianchen, Liang, Weihang, McPherson, D. Livingston, Driggs-Campbell, Katherine
In autonomous driving, detection of abnormal driving behaviors is essential to ensure the safety of vehicle controllers. Prior works in vehicle anomaly detection have shown that modeling interactions between agents improves detection accuracy, but ce
Externí odkaz:
http://arxiv.org/abs/2301.03634
Autor:
Liu, Shuijing, Chang, Peixin, Huang, Zhe, Chakraborty, Neeloy, Hong, Kaiwen, Liang, Weihang, McPherson, D. Livingston, Geng, Junyi, Driggs-Campbell, Katherine
We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the intentions of p
Externí odkaz:
http://arxiv.org/abs/2203.01821
Navigation through uncontrolled intersections is one of the key challenges for autonomous vehicles. Identifying the subtle differences in hidden traits of other drivers can bring significant benefits when navigating in such environments. We propose a
Externí odkaz:
http://arxiv.org/abs/2109.06783