  Despite all the challenges and limitations, vision-based vehicle speed
detection is gaining research interest due to its great potential benefits such
as cost reduction, and enhanced additional functions. As stated in a recent
survey [1], the use of learning-based approaches to address this problem is
still in its infancy. One of the main difficulties is the need for a large
amount of data, which must contain the input sequences and, more importantly,
the output values corresponding to the actual speed of the vehicles. Data
collection in this context requires a complex and costly setup to capture the
images from the camera synchronized with a high precision speed sensor to
generate the ground truth speed values. In this paper we explore, for the first
time, the use of synthetic images generated from a driving simulator (e.g.,
CARLA) to address vehicle speed detection using a learning-based approach. We
simulate a virtual camera placed over a stretch of road, and generate thousands
of images with variability corresponding to multiple speeds, different vehicle
types and colors, and lighting and weather conditions. Two different approaches
to map the sequence of images to an output speed (regression) are studied,
including CNN-GRU and 3D-CNN. We present preliminary results that support the
high potential of this approach to address vehicle speed detection.
