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Found 5 papers, 2 papers with code
Risk assessment and mitigation of e-scooter crashes with naturalistic driving data
The goal is to quantitatively measure the behaviors of e-scooter riders in different encounters to help facilitate crash scenario modeling, baseline behavior modeling, and the potential future development of in-vehicle mitigation algorithms.
A Wearable Data Collection System for Studying Micro-Level E-Scooter Behavior in Naturalistic Road Environment
As one of the most popular micro-mobility options, e-scooters are spreading in hundreds of big cities and college towns in the US and worldwide.
Peek into the Future Camera-based Occupant Sensing in Configurable Cabins for Autonomous Vehicles
The results show the needs and design of a different number of cameras to fully or partially cover all the occupants with changeable configurations of up to six seats.
PSI: A Pedestrian Behavior Dataset for Socially Intelligent Autonomous Car
These innovative labels can enable several computer vision tasks, including pedestrian intent/behavior prediction, vehicle-pedestrian interaction segmentation, and video-to-language mapping for explainable algorithms.
Detection of E-scooter Riders in Naturalistic Scenes
We fine-tune MobileNetV2 over our dataset and train the model to classify e-scooter riders and pedestrians.
