  LiDAR sensors used in autonomous driving applications are negatively affected
by adverse weather conditions. One common, but understudied effect, is the
condensation of vehicle gas exhaust in cold weather. This everyday phenomenon
can severely impact the quality of LiDAR measurements, resulting in a less
accurate environment perception by creating artifacts like ghost object
detections. In the literature, the semantic segmentation of adverse weather
effects like rain and fog is achieved using learning-based approaches. However,
such methods require large sets of labeled data, which can be extremely
expensive and laborious to get. We address this problem by presenting a
two-step approach for the detection of condensed vehicle gas exhaust. First, we
identify for each vehicle in a scene its emission area and detect gas exhaust
if present. Then, isolated clouds are detected by modeling through time the
regions of space where gas exhaust is likely to be present. We test our method
on real urban data, showing that our approach can reliably detect gas exhaust
in different scenarios, making it appealing for offline pre-labeling and online
applications such as ghost object detection.
