Machine Intelligence for Driver Safety

TrueMotion’s technology platform uses smartphone sensor data to distinguish between safe and risky driving and redefine how insurance is priced and delivered. We will discuss the intrinsic challenges of determining driver safety with a smartphone, such as distinguishing driving from other forms of transport, determining whether the owner of a phone is a driver or passenger in a car, and detecting distracted driving and vehicle dynamics using unreliable smartphone IMUs, all while minimizing battery usage and user interaction. We will also explore the algorithms that TrueMotion has developed to address these challenges.

Dan Shiebler, Data Scientist at True Motion

Dan is a Data Scientist at TrueMotion, where he builds machine learning algorithms that use smartphone sensors to understand and score driving behaviors. Dan leads TrueMotion's efforts on developing smartphone IMU algorithms to detect hard brakes and distracted driving. Dan is also a guest speaker at the NYC Data Science Academy. In the past, he has worked as a neurosurgery researcher at Rhode Island Hospital, as a Digital Humanities Programmer at the Brown University Library, and as a Computational Biology Software Consultant for the Weinreich Lab at Brown University. Dan graduated from Brown University in 2015.

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