Zobrazeno 1 - 10
of 426
pro vyhledávání: '"Di Xuan"'
From Twitter to Reasoner: Understand Mobility Travel Modes and Sentiment Using Large Language Models
Social media has become an important platform for people to express their opinions towards transportation services and infrastructure, which holds the potential for researchers to gain a deeper understanding of individuals' travel choices, for transp
Externí odkaz:
http://arxiv.org/abs/2411.02666
The advancement of autonomous driving technologies necessitates increasingly sophisticated methods for understanding and predicting real-world scenarios. Vision language models (VLMs) are emerging as revolutionary tools with significant potential to
Externí odkaz:
http://arxiv.org/abs/2408.16647
Autonomous driving training requires a diverse range of datasets encompassing various traffic conditions, weather scenarios, and road types. Traditional data augmentation methods often struggle to generate datasets that represent rare occurrences. To
Externí odkaz:
http://arxiv.org/abs/2408.15868
Social norm is defined as a shared standard of acceptable behavior in a society. The emergence of social norms fosters coordination among agents without any hard-coded rules, which is crucial for the large-scale deployment of AVs in an intelligent tr
Externí odkaz:
http://arxiv.org/abs/2408.12680
Mean field games (MFGs) model the interactions within a large-population multi-agent system using the population distribution. Traditional learning methods for MFGs are based on fixed-point iteration (FPI), which calculates best responses and induced
Externí odkaz:
http://arxiv.org/abs/2408.08192
Imagine there is a disruption in train 1 near Times Square metro station. You try to find an alternative subway route to the JFK airport on Google Maps, but the app fails to provide a suitable recommendation that takes into account the disruption and
Externí odkaz:
http://arxiv.org/abs/2407.14926
We propose a discrete time graphon game formulation on continuous state and action spaces using a representative player to study stochastic games with heterogeneous interaction among agents. This formulation admits both philosophical and mathematical
Externí odkaz:
http://arxiv.org/abs/2405.08005
Mean field games (MFGs) are a promising framework for modeling the behavior of large-population systems. However, solving MFGs can be challenging due to the coupling of forward population evolution and backward agent dynamics. Typically, obtaining me
Externí odkaz:
http://arxiv.org/abs/2405.03718
This paper aims to develop a learning method for a special class of traveling salesman problems (TSP), namely, the pickup-and-delivery TSP (PDTSP), which finds the shortest tour along a sequence of one-to-one pickup-and-delivery nodes. One-to-one her
Externí odkaz:
http://arxiv.org/abs/2404.11458
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks
Externí odkaz:
http://arxiv.org/abs/2303.02063