AccidentGPT: Accident analysis and prevention from V2X environmental perception with multi-modal large model
Traffic accidents, being a significant contributor to both human casualties and property
damage, have long been a focal point of research for many scholars in the field of traffic …
damage, have long been a focal point of research for many scholars in the field of traffic …
Large language models for mobility in transportation systems: A survey on forecasting tasks
Mobility analysis is a crucial element in the research area of transportation systems.
Forecasting traffic information offers a viable solution to address the conflict between …
Forecasting traffic information offers a viable solution to address the conflict between …
Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
Foundation models have indeed made a profound impact on various fields, emerging as
pivotal components that significantly shape the capabilities of intelligent systems. In the …
pivotal components that significantly shape the capabilities of intelligent systems. In the …
Enhancing accuracy in large language models through dynamic real-time information injection
Q Ouyang, S Wang, B Wang - 2023 - preprints.org
This study presents a novel approach to enhance Large Language Models (LLMs) like
Alpaca by dynamically integrating real-time information. This method addresses the issue of …
Alpaca by dynamically integrating real-time information. This method addresses the issue of …
An Intelligent Deep Learning Framework for Traffic Flow Imputation and Short-term Prediction Based on Dynamic Features
The accurate prediction of traffic flow has emerged as a focal point in the cutting-edge
sphere of intelligent transportation. Extant methodologies rely on deep learning for short …
sphere of intelligent transportation. Extant methodologies rely on deep learning for short …
Generating and Evolving Reward Functions for Highway Driving with Large Language Models
Reinforcement Learning (RL) plays a crucial role in advancing autonomous driving
technologies by maximizing reward functions to achieve the optimal policy. However …
technologies by maximizing reward functions to achieve the optimal policy. However …
Feasibility of State Space Models for Network Traffic Generation
Many problems in computer networking rely on parsing collections of network traces (eg,
traffic prioritization, intrusion detection). Unfortunately, the availability and utility of these …
traffic prioritization, intrusion detection). Unfortunately, the availability and utility of these …
UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models
Location-based services play an critical role in improving the quality of our daily lives.
Despite the proliferation of numerous specialized AI models within spatio-temporal context …
Despite the proliferation of numerous specialized AI models within spatio-temporal context …
CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System
The spatiotemporal data generated by massive sensors in the Internet of Things (IoT) is
extremely dynamic, heterogeneous, large scale and time-dependent. It poses great …
extremely dynamic, heterogeneous, large scale and time-dependent. It poses great …
Research on Foundation Model for Spatial Data Intelligence: China's 2024 White Paper on Strategic Development of Spatial Data Intelligence
This report focuses on spatial data intelligent large models, delving into the principles,
methods, and cutting-edge applications of these models. It provides an in-depth discussion …
methods, and cutting-edge applications of these models. It provides an in-depth discussion …