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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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How They Did It: An Analysis of Emission Defeat Devices in Modern Automobiles
Modern vehicles are required to comply with a range of environmental regulations limiting the level of emissions for various greenhouse gases, toxins and particulate matter. To ensure compliance, regulators test vehicles in controlled settings and empirically measure their emissions at the tailpipe. However, the black box nature of this testing and the standardization of its forms have created an opportunity for evasion. Using modern electronic engine controllers, manufacturers can programmatically infer when a car is undergoing an emission test and alter the behavior of the vehicle to comply with emission standards, while exceeding them during normal driving in favor of improved performance. While the use of such a defeat device by Volkswagen has brought the issue of emissions cheating to the public's attention, there have been few details about the precise nature of the defeat device, how it came to be, and its effect on vehicle behavior. In this paper, we present our analysis of two families of software defeat devices for diesel engines: one used by the Volkswagen Group to pass emissions tests in the US and Europe, and a second that we have found in Fiat Chrysler Automobiles. To carry out this analysis, we developed new static analysis firmware forensics techniques necessary to automatically identify known defeat devices and confirm their function. We tested about 900 firmware images and were able to detect a potential defeat device in more than 400 firmware images spanning eight years. We describe the precise conditions used by the firmware to detect a test cycle and how it affects engine behavior. This work frames the technical challenges faced by regulators going forward and highlights the important research agenda in providing focused software assurance in the presence of adversarial manufacturers.
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- Award ID(s):
- 1646493
- PAR ID:
- 10029049
- Date Published:
- Journal Name:
- IEEE Symposium on Security and Privacy
- Page Range / eLocation ID:
- 231 to 250
- 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
- Conference Paper:
- https://doi.org/10.1109/SP.2017.66
