Secondary crashes, including struck-by incidents are a leading cause of line-of-duty deaths among emergency responders, such as firefighters, law enforcement officers, and emergency medical service providers. The introduction of light-emitting diode (LED) sources and advanced lighting control systems provides a wide range of options for emergency lighting configurations. This study investigated the impact of lighting color, intensity, modulation, and flash rate on driver behavior while traversing a traffic incident scene at night. The impact of retroreflective chevron markings in combination with lighting configurations, as well as the measurement of “moth-to-flame” effects of emergency lighting on drivers was also investigated. This human factors study recruited volunteers to drive a closed course traffic incident scene, at night under various experimental conditions. The simulated traffic incident was designed to replicate a fire apparatus in the center-block position. The incident

D. Bullough, John, Parr, Scott, Hiebner, Emily, Sblendorio, Alec

Windshield Defrosting Systems Test Procedure and Performance Requirements - Trucks, Buses, and Multipurpose Vehicles

J381_202502 (Current)

02/25/2025

This SAE Recommended Practice establishes uniform test procedures and performance requirements for the defrosting system of enclosed cab trucks, buses, and multipurpose vehicles. It is limited to a test that can be conducted on uniform test equipment in commercially available laboratory facilities. For laboratory evaluation of defroster systems, current engineering practice prescribes that an ice coating of known thickness be applied to the windshield and left- and right-hand side windows to provide more uniform and repeatable test results, even though - under actual conditions - such a coating would necessarily be scraped off before driving. The test condition, therefore, represents a more severe condition than the actual condition, where the defroster system must merely be capable of maintaining a cleared viewing area. Because of the special nature of the operation of most of these vehicles (where vehicles are generally kept in a garage or warmed up before driving), and since

Truck and Bus Windshield Wipers and Climate Control Comm

Windshield Wiper Systems - Trucks, Buses, and Multipurpose Vehicles

J198_202502 (Current)

02/25/2025

This SAE Recommended Practice establishes testing methods and performance requirements for windshield wiping systems on trucks, buses, and multipurpose passenger vehicles with a GVWR of 4500 kg (10000 pounds) or greater and light-duty utility vehicles with a GVWR of less than 4500 kg (10000 pounds). The test procedures and minimum performance requirements outlined in this document are based on currently available engineering data. It is the intent that all portions of the document will be periodically reviewed and revised as additional data regarding windshield wiping system performance are developed.

Truck and Bus Windshield Wipers and Climate Control Comm

Classification of Pavement Distress Images Using Fusion Convolutional Neural Network of Dual Branch

2025-01-7131

02/21/2025

In recent years, the issue of highway maintenance has become increasingly prominent. How to precisely detect and classify fine cracks and various types of pavement defects on highways through technical means is an essential foundation for achieving intelligent road maintenance. This paper first constructs the DenseNet201-PDC and MobileNetV2-PDC sub-classification networks that incorporate the three-channel attention judgment mechanism MCA. Secondly, based on the principle of parallel connection, a brand-new dual-branch fusion convolutional neural network DBF-PDC capable of classifying pavement defects in highway scenarios is proposed. Finally, this paper builds the Pavement Distress Datasets of Southeast University and conducts relevant ablation experiments. The experimental results demonstrate that both the attention mechanism module and the feature fusion strategy can significantly enhance the network's ability to classify pavement defects in highway scenarios. The average

Zhang, Ziyi, Zhao, Chihang, Shao, Yongjun, Wang, Junjun

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