WatchPed: Pedestrian Crossing Intention Prediction Using Embedded Sensors of Smartwatch

Understand

The pedestrian crossing intention prediction problem is to estimate whether or not the target pedestrian will cross the street.

• State-of-the-art techniques heavily depend on visual data acquired through the front camera of the ego-vehicle to make a prediction of the pedestrian's crossing intention.
• Hence, the efficiency of current methodologies tends to decrease notably in situations where visual input is imprecise, for instance, when the distance between the pedestrian and ego-vehicle is considerable or the illumination levels are inadequate.
• To address the limitation, in this paper, we present the design, implementation, and evaluation of the first-of-its-kind pedestrian crossing intention prediction model based on integration of motion sensor data gathered through the smartwatch (or smartphone) of the pedestrian.