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An experimental approach analyzing who sees disorder when there is nothing to see: understanding variance of perceptions via personal characteristics

Description
Knowing that disorder is related to crime, it has become essential for criminologists to understand how and why certain individuals perceive disorder. Using data from the Perceptions of Neighborhood Disorder and Interpersonal Conflict Project, this study uses a fixed photograph of a neighborhood, to assess whether individuals "see" disorder cues.…

Knowing that disorder is related to crime, it has become essential for criminologists to understand how and why certain individuals perceive disorder. Using data from the Perceptions of Neighborhood Disorder and Interpersonal Conflict Project, this study uses a fixed photograph of a neighborhood, to assess whether individuals "see" disorder cues. A final sample size of n=815 respondents were asked to indicate if they saw particular disorder cues in the photograph. The results show that certain personal characteristics do predict whether an individual sees disorder. Because of the experimental design, results are a product of the individual's personal characteristics, not of the respondent's neighborhood. These findings suggest that the perception of disorder is not as clear cut as once thought. Future research should explore what about these personal characteristics foster the perception of disorder when it is not present, as well as, how to fight disorder in neighborhoods when perception plays such a substantial role.
ContributorsScott, Christopher (Author) / Wallace, Danielle (Thesis advisor) / Katz, Charles (Committee member) / Ready, Justin (Committee member) / Arizona State University (Publisher)
Created2013
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Traffic light status detection using movement patterns of vehicles

Description
Traditional methods for detecting the status of traffic lights used in autonomous vehicles may be susceptible to errors, which is troublesome in a safety-critical environment. In the case of vision-based recognition methods, failures may arise due to disturbances in the environment such as occluded views or poor lighting conditions. Some…

Traditional methods for detecting the status of traffic lights used in autonomous vehicles may be susceptible to errors, which is troublesome in a safety-critical environment. In the case of vision-based recognition methods, failures may arise due to disturbances in the environment such as occluded views or poor lighting conditions. Some methods also depend on high-precision meta-data which is not always available. This thesis proposes a complementary detection approach based on an entirely new source of information: the movement patterns of other nearby vehicles. This approach is robust to traditional sources of error, and may serve as a viable supplemental detection method. Several different classification models are presented for inferring traffic light status based on these patterns. Their performance is evaluated over real-world and simulation data sets, resulting in up to 97% accuracy in each set.
ContributorsCampbell, Joseph (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Heni (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2016