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1.
Hum Factors ; 62(2): 249-259, 2020 03.
Article in English | MEDLINE | ID: mdl-31502899

ABSTRACT

OBJECTIVE: This research evaluated Automated Driving Systems (ADSs) involved collisions to identify factors relevant to future ADS research and development. BACKGROUND: Rapidly developing ADSs promise improved safety, among other benefits. Properly applied collision research can inform ADS development, to minimize future collisions. Errors and failures that result in collisions come from sources including the system, ADS operators, and external factors including other drivers. Partially automated systems incorporate new equipment and procedures creating new sources of human error. Fully autonomous systems represent a new class of drivers that interact in unique ways. METHOD: ADS collision reports from the California Department of Motor Vehicles and the National Transportation Safety Board were collected. An expert in human factors and collision investigation analyzed and categorized the crashes while extracting common factors. RESULTS: ADS vehicles were never at fault but were often affected from the rear during braking, turning, and gap acceptance maneuvers. Side impacts to ADS vehicles were related to passing vehicles and lane keeping behaviors. Unique incidents also provided additional insights. ADS collision rates cannot yet be determined with confidence. CONCLUSION: Conflicts that lead to collision-involvement with ADSs may be caused by differences between ADS and human driving behavior. Conservative ADS behavior may violate the expectations of other nearby human road users. APPLICATION: The findings from this work help inform the future development of ADS, as well as potentially the testing of ADS and the formation of policy to guide their future deployment.


Subject(s)
Accidents, Traffic/prevention & control , Automation , Automobile Driving/psychology , Automobiles , Man-Machine Systems , Attention , Equipment Failure , Humans , Motivation , Risk Factors
2.
Accid Anal Prev ; 38(5): 895-906, 2006 Sep.
Article in English | MEDLINE | ID: mdl-16584702

ABSTRACT

Crash causation research has identified inattention as a major source of driver error leading to crashes. The series of experiments presented herein investigate the characteristics of an in-vehicle information system (IVIS) task that could hinder driving performance due to uncertainty buildup and cognitive capture. Three on-road studies were performed that used instrumented passenger and tractor-trailer vehicles to obtain real-world driving performance data. Participants included young, middle-aged, and older passenger vehicle drivers and middle-aged and older commercial vehicle operators. While driving, they were presented with IVIS tasks with various information densities, decision-making elements, presentation formats, and presentation modalities (visual or auditory). The experiments showed that, for both presentation modalities, the presence of multiple decision-making elements in a task had a substantial negative impact on driving performance of both automobile drivers and truck drivers when compared to conventional tasks or tasks with only one decision-making element. The results from these experiments can be used to improve IVIS designs, allowing for potential IVIS task phenomena such as uncertainty buildup and cognitive capture to be avoided.


Subject(s)
Automobile Driving , Task Performance and Analysis , Adolescent , Adult , Age Factors , Aged , Auditory Perception , Cognition , Data Display , Decision Making , Female , Humans , Male , Middle Aged , Visual Perception
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