Head dynamics enabled riding maneuver prediction

Abstract

While micro-mobility brings convenience to the modern city, they also cause various social problems such as trac accidents, casualties, and huge economic losses. Wearing protective equipment has become the primary recommendation for safe riding, but passive protection cannot prevent accidents from happening after all. Thus, timely predicting the rider's maneuver is essential for more active protection and buying more time to avoid potential accidents from happening. In this poster, we explore the feasibility of using riders' head dynamics to predict their riding maneuvers. Through ten participants' preliminary study, we observed that not only do riders' head movements appear ahead of their maneuvers but also head movement patterns are distinct with dierent maneuver intentions. We then construct a deep learning network using Long Short Term Memory, achieving 89% of accuracy on maneuver prediction. CCS CONCEPTS• Human-centered computing ! Ubiquitous and mobile computing systems and tools.

Towards safer mobility in cities and communities: a framework to assist the design process of cycling warning systems

“…(Costa et al 2018;Bernardes et al 2019;Nuñez et al 2018;Matviienko et al 2022;Jin et al 2021;von Sawitzky et al 2022;Han et al …”