Latest posts tagged with #COMMTR on Bluesky
π Call for Papers β #COMMTR Special Issue
"Foundation Models for Intelligent Control in Autonomous Driving Traffic Systems"
COMMTR welcomes submissions exploring how #LLMs, #VLMs, and #multimodalfoundationmodels are advancing autonomous driving and intelligent traffic systems. π€
#COMMTR: New study shows how disruptions impact urban transit accessibility! Actual vs. scheduled data reveal big differences. Big data can help improve equity.
Link: doi.org/10.1016/j.co... ππ
#PublicTransport #BigData @Tsinghua_Uni @Unibo @HeidelbergU
Fig. 1. Determinant of driver capability.
#COMMTR online: πβ¨ A study by The University of Queensland redefines how we measure driver capability through desired time headway, showing it varies with speed and environment. Connected tech boosts safety!
π π : doi.org/10.1016/j.co...
#TrafficSafety #SmartDriving
#COMMTR online: π Researchers from #HKUST and Imperial College London have developed a new method using #deepreinforcement learning to design fuel- and noise-minimal aircraft departure trajectories, considering aircraft dynamics and topography constraints!
πβοΈ more: doi.org/10.1016/j.co...
@scopus
Fig. 1. Comparison of (a) the conventional learning method (this method achieves centralized learning by uploading the data that may be sensitive and thus will harm privacy) and (b) our proposed federated learning method (this method achieves federated learning by uploading the model parameter instead of initial data and thus improves security and protects privacy.).
#COMMTR online: New research from Carnegie Mellon University and Morgan State University uses #federated learning to boost cybersecurity and privacy in cooperative driving automation. π
π‘ Check out: doi.org/10.1016/j.co... #AutonomousVehicles #FederatedLearning
Fig. 1. Architecture of the deep learning model used in this study for trafο¬c forecasting.
#COMMTR online: Exciting new research on making traffic forecasting models more interpretable! π
Rushan Wang, Yanan Xin, and their team developed a framework using counterfactual explanations to enhance #DLmodels.
π Check it out: buff.ly/1YI3ygG
#AI #TrafficPrediction
Recently published online! Ingrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning βοΈAuthors: Zhiheng Li , Shen Li , Li Li et al
π more doi.org/10.1016/j.co... #transportation #Trajectory planning #commtr
π Weβve officially joined Bluesky!
Starting today, #JICV and #COMMTR journals are now on Bluesky! π
πFollow us as we share cutting-edge research in #autonomousvehicles, #transportation , and AI technologies.
Letβs connect and discuss the latest breakthroughs in smart transportation and mobility!
