Most fatal road accidents in urban areas involve vulnerable users. New solutions for fighting against these can be considered by leveraging connected, intelligent vehicles and smart cities connecting all parts of an environment. This work proposes a multi-sensing communication approach to prevent potential between VRUs, predicting notifying both about collisions before they happen. leverages ag...

Prediction models have been extensively used in the field of road safety, however, none these particularly applied to zero-emission electric vehicle (EV) related injuries so far; which may lead different outcomes due their inaudible engines. Using an optimizable classification tree, this first-ever study aims predict likelihood personal injury severities stemming from EV-related crashes on Brit...

The potential effectiveness of vehicle-based secondary safety systems for the protection of pedestrians and pedal cyclists is related to the proportion of cases where injury arises by contact with the road or ground rather than with the striking vehicle. A detailed case review of 205 accidents from the UK On-the-Spot study involving vulnerable road users with head injuries or impacts indicated ...

The development of vehicular electronic vision systems for the automotive market is a growing field, driven in particular by customer demand to increase the safety of vehicles both for drivers, and for other road users, including Vulnerable Road Users (VRUs), such as pedestrians. Close-range automotive camera systems are designed to display the areas in the close vicinity of the vehicle to the ...

In this work, orientation detection using Deep Learning is acknowledged for a particularly vulnerable class of road users,the cyclists. Knowing the cyclists' great relevance since it provides good notion about their future trajectory, which crucial to avoid accidents in context intelligent transportation systems. Using Transfer with pre-trained models and TensorFlow, we present performance comp...