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Interacting with bicycles on urban road segments is complex for vehicles, due to the diverse and flexible cycling behavior that can cause interferences. Studies show that driving mixed with bicycles is also a great challenge for autonomous vehicles (AVs), and it is necessary to consider the interference of bicycles when selecting public roads for testing. However, existing road evaluation methods mostly focus on autonomous driving functions and accident analysis, although bicycles have been considered, often with insufficient consideration of their interference. This study analyzes two types of cycling behavior that could interfere with vehicles, including lateral (turning handlebars) and longitudinal (braking or accelerating) behavior, with each occurrence of such behavior considered as one potential lateral or longitudinal interference. From the perspective of cycling behavior, a framework is proposed to assess the complexity of potential bicycle interference on vehicles on road segments. A higher frequency of both potential lateral and longitudinal interference represents a higher complexity of potential interference. A naturalistic field experiment was conducted to collect the potential lateral and longitudinal interference frequency and the environmental parameters of road segments. The quantile regression model was applied to analyze the environmental factors influencing different interference frequencies separately and further establish the assessing model of the potential bicycle interference complexity, and the usability of the model has been demonstrated with a case study. Results show that the potential interference complexity varies across road segments, with some factors leading to more frequent potential lateral and longitudinal interference but with varying degrees of impact (such as the separation between bicycles and vehicles), while some only affect the lateral interference frequency (such as the on-street parking condition). The proposed framework can help autonomous driving companies or evaluation agencies to select appropriate testing roads, thus promoting the development of autonomous driving. [ABSTRACT FROM AUTHOR]

The phase equilibriums of the ternary system KCl-(NH2)2CO-H2O at 303.15 K, 323.15 K, 333.15 K, and 343.15 K were studied using the isothermal dissolution equilibrium method, in which the composition of equilibrium solid phase was determined by Schreinemaker's wet residue method and X-ray diffraction (XRD) method. It was found that the ternary system is a simple cosaturated system, without the formation of neither double salt nor solid solution. Wilson and NRTL models were employed to correlate in the solubility data of the system at experimental temperatures. The maximum values of RAD and RMSD of the Wilson model were 2.18 × 10−2 and 0.83 and those of the NRTL model were 1.69 × 10−2 and 0.40, respectively. The two models were utilized to forecast solubility data at various temperatures, and the obtained outcomes were in line with the literature data. Based on the experimental solubility data at 343.15 K, the cooling crystallization process of the system was monitored online by focused beam reflectance measurement (FBRM) and particle video microscope (PVM). The crystal products were characterized by XRD, scanning electron microscope (SEM), and energy dispersive spectrometry (EDS). The results showed that the precipitation of (NH2)2CO occurred during the crystallization process, and this was followed by KCl. KCl was formed on the surface of (NH2)2CO crystal. The crystal was a simple mixture containing KCl and (NH2)2CO. [ABSTRACT FROM AUTHOR]