  Accurate vision-based speed estimation is much more cost-effective than
traditional methods based on radar or LiDAR. However, it is also challenging
due to the limitations of perspective projection on a discrete sensor, as well
as the high sensitivity to calibration, lighting and weather conditions.
Interestingly, deep learning approaches (which dominate the field of computer
vision) are very limited in this context due to the lack of available data.
Indeed, obtaining video sequences of real road traffic with accurate speed
values associated with each vehicle is very complex and costly, and the number
of available datasets is very limited. Recently, some approaches are focusing
on the use of synthetic data. However, it is still unclear how models trained
on synthetic data can be effectively applied to real world conditions. In this
work, we propose the use of digital-twins using CARLA simulator to generate a
large dataset representative of a specific real-world camera. The synthetic
dataset contains a large variability of vehicle types, colours, speeds,
lighting and weather conditions. A 3D CNN model is trained on the digital twin
and tested on the real sequences. Unlike previous approaches that generate
multi-camera sequences, we found that the gap between the the real and the
virtual conditions is a key factor in obtaining low speed estimation errors.
Even with a preliminary approach, the mean absolute error obtained remains
below 3km/h.
