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ESRA E-Survey of Road users' Attitudes:Cyclists

• Authors : Møller, Mette; Jensen, Thomas Christian

Subjects: book

• Source: Møller , M & Jensen , T C 2024 , ESRA E-Survey of Road users' Attitudes : Cyclists .

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Using Artificial Intelligence to Improve Safety for Vulnerable Road Users

Subjects: United StatesTraffic safety; Artificial intelligence; Vulnerable road users

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October 2024 R&T Now Interchanges: Virtual Reality Helps Keep Vulnerable Road Users Safe

Subjects: United StatesVirtual reality; Vulnerable road users; Traffic safety

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Safety Analysis and Behaviour Modelling of Vulnerable Road Users

• Authors : Zhang, Cheng

Subjects: Pedestrian; Cyclist; Vulnerable road user

• Source: University of Wollongong Thesis Collection 2017+

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RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models

• Authors : Hussien, Mohamed Manzour; Melo, Angie Nataly; Ballardini, Augusto Luis

Subjects: Computer Science - Machine Learning; Computer Science - Artificial Intelligence; Computer Science - Computation and Language

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Cognitive Internet of Vulnerable Road Users in Traffic: Predictive Neural Modulations of Road Crossing Intention

• Authors : Zhou, Xiaoshan; Menassa, Carol C.; Kamat, Vineet R.

Subjects: Computer Science - Human-Computer Interaction; Computer Science - Emerging Technologies; text

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Mitigating Vulnerable Road Users Occlusion Risk Via Collective Perception: An Empirical Analysis

• Authors : Wolff, Vincent Albert; Xhoxhi, Edmir

Subjects: Computer Science - Networking and Internet Architecture; Computer Science - Machine Learning; Computer Science - Robotics

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Control of Automated Vehicles in Vehicle-Pedestrian Environment

Subjects: United StatesPedestrians; Unsignalized intersections; Vulnerable road users

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V2X Intention Sharing for E-bikes and E-scooters : Design and implementation of a vehicular network protocol for Vulnerable Road Users intention sharing.

• Authors : Elfing, Johan; Pålsson, Joel

Subjects: ETSI; Intelligent Transport Systems; Vulnerable Road Users

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LiMTR: Using local LiDAR features for Road Users Motion Prediction

• Authors : Oerlemans, Camiel H.J.M.

Subjects: Master; Master

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