  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. We propose an
innovative machine learning framework that effectively integrates motion sensor
data with visual input to enhance the predictive accuracy significantly,
particularly in scenarios where visual data may be unreliable. Moreover, we
perform an extensive data collection process and introduce the first pedestrian
intention prediction dataset that features synchronized motion sensor data. The
dataset comprises 255 video clips that encompass diverse distances and lighting
conditions. We trained our model using the widely-used JAAD and our own
datasets and compare the performance with a state-of-the-art model. The results
demonstrate that our model outperforms the current state-of-the-art method,
particularly in cases where the distance between the pedestrian and the
observer is considerable (more than 70 meters) and the lighting conditions are
inadequate.
