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Unnecessary vehicle idling negatively contributes to air quality, which harms human health. The latter harms are greater when vehicles concentrate in an area frequented by vulnerable populations, such as children and hospital patients. The present experiments evaluated the effects of social-norm messages presented in a hypothetical school pickup zone on online drivers’ intent to idle. In Experiment 1, when messages were described as presented on a dynamic feedback display, much like those used to reduce speeding, they significantly decreased intent to idle. This effect was larger when a picture of a child accompanied the message. In Experiment 2, the social norm message plus picture significantly decreased intent to idle when four or fewer other drivers in the area were described as idling (i.e., ignoring the injunctive social-norm message). Future planned research will evaluate the efficacy of this dynamic display in reducing real idling behavior in high-idling zones frequented by vulnerable populations.
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Real-Time Idling Vehicles Detection Using Combined Audio-Visual Deep Learning
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 a 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 multisensor, 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 detection and 91.06 for engine off detection.
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- Award ID(s):
- 1952008
- PAR ID:
- 10621688
- Publisher / Repository:
- IOS Press
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Book Chapter:
- https://doi.org/10.3233/ATDE240029
