A numerical investigation of e-scooter-to-vehicle traffic accidents.

Within the past decade, injuries caused by electric scooter (e-scooter) crashes have significantly increased. A common cause of fatalities for e-scooter riders is a collision between a car and an e-scooter. To develop a better understanding of the complex injury mechanisms in these collisions, four crashes between an e-scooter and a family car/sedan and a sports utility vehicle were simulated using finite element models.

The Impact of Line-of-Sight and Connected Vehicle Technology on Mitigating and Preventing Crash and Near-Crash Events.

Line-of-sight (LOS) sensors developed in newer vehicles have the potential to help avoid crash and near-crash scenarios with advanced driving-assistance systems; furthermore, connected vehicle technologies (CVT) also have a promising role in advancing vehicle safety. This study used crash and near-crash events from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) to reconstruct crash events so that the applicable benefit of sensors in LOS systems and CVT can be compared. The benefits of CVT over LOS systems include additional reaction time before a predicted crash, as well as a lower deceleration value needed to prevent a crash.

A Numerical Investigation of Rider Injury Risks During Falls Caused by E-Scooter-Stopper Impacts.

Within the past decade, injuries caused by electric scooter (e-scooter) crashes have significantly increased. A primary cause is front wheel collisions with a vertical surface such as a curb or object, generically referred to as a "stopper." In this study, various e-scooter-stopper crashes were simulated numerically across different impact speeds, approach angles, and stopper heights to characterize the influence of crash type on rider injury risk during falls.

Decision-adjusted driver risk predictive models using kinematics information.

Accurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly available from connected vehicle technology, to obtain dense risk predictors. In this work, we propose a decision-adjusted framework to develop optimal driver risk prediction models using telematics-based driving behavior information.