Decision-making at intersections
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R. Sutton and L. Vangsness, "Decision-Making at Intersections," 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS), Orlando, FL, USA, 2022, pp. 1-5, doi: 10.1109/ICHMS56717.2022.9980722.
As vehicles become increasingly automated, it is important to consider how drivers’ behaviors are impacted by factors such as trust in automation and risk. Early definitions of trust suggest that peoples’ perceptions of risk matter more than the objective reality. This study was designed to assess this claim by asking individuals to make braking judgments within the context of a driver approaching a yellow light at an intersection. The judgments were made with and without an automated braking system. We predicted that objective risk would inform drivers’ perceptions of risk and make them more likely to endorse braking as they neared the intersection. We also predicted that drivers’ overall attitudes towards braking would differ when they were instructed to imagine using an automated braking system versus when they imagined driving the vehicle themselves. These hypotheses were tested with a Qualtrics survey, where drivers viewed images that portrayed a car at various distances from a yellow light. Each image was paired with a Likert-style question to assess drivers’ endorsement of braking at the point depicted in the figure. Participants’ endorsements of braking increased as the vehicle was depicted further from the intersection. This pattern of endorsement was similar when participants imagined driving the automated car; however, participants had a bias to endorse braking more strongly when they were tasked with driving a car without automation. These results suggest that people may see automated braking systems as behaving differently from themselves, a finding that has meaningful implications for systems design.
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