  Combustion vehicle emissions contribute to poor air quality and release
greenhouse gases into the atmosphere, and vehicle pollution has been associated
with numerous adverse health effects. Roadways with extensive waiting and/or
passenger drop off, such as schools and hospital drop-off zones, can result in
high incidence and density of idling vehicles. This can produce micro-climates
of increased vehicle pollution. Thus, the detection of idling vehicles can be
helpful in monitoring and responding to unnecessary idling and be integrated
into real-time or off-line systems to address the resulting pollution. In this
paper we present a real-time, dynamic vehicle idling detection algorithm. The
proposed idle detection algorithm and notification rely on an algorithm to
detect these idling vehicles. The proposed method relies on a multi-sensor,
audio-visual, machine-learning workflow to detect idling vehicles visually
under three conditions: moving, static with the engine on, and static with the
engine off. The visual vehicle motion detector is built in the first stage, and
then a contrastive-learning-based latent space is trained for classifying
static vehicle engine sound. We test our system in real-time at a hospital
drop-off point in Salt Lake City. This in-situ dataset was collected and
annotated, and it includes vehicles of varying models and types. The
experiments show that the method can detect engine switching on or off
instantly and achieves 71.02 average precision (AP) for idle detections and
91.06 for engine off detections.
