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Do You Trust Me? Assessing Reliability for Driver-Assisted Autonomous Vehicles (2023-US-30MP-1410)Caleb King, Research Statistician Developer, JMP
Autonomous vehicles, or self-driving cars, no longer only live in science fiction. Engineers and scientists are making them a reality. However, their reliability concerns, or more importantly, safety concerns, have been crucial to their commercial success. Can we trust autonomous vehicles? Do we have the information to make this decision?In this talk, we investigate the reliability of autonomous vehicles (AVs) produced by four leading manufacturers by analyzing the publicly available data that have been submitted to the California DMV AV testing program. We assess the quality of the data, evaluate the amount of information contained in the data, analyze the data in various ways, and eventually attempt to draw some conclusions from what we have learned in the process. We show how we utilized various tools in JMP in this study, including processing the raw data, establishing assumptions and limitations of the data, fitting different reliability models, and finally, selecting appropriate models to draw conclusions. The limitations of the data include both quality and quantity. As such, our results might be far from conclusive, but we can still gain important insights with proper statistical methodologies.