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Making Roads Safer by Making Drivers Better

Published: 04 October 2017 Publication History

Abstract

The world's roads see over 50 million injuries and 1.25 million fatalities every year; road accidents are the leading cause of death among people between the ages of 15 to 30. This talk will describe how mobile sensing (especially using smartphones), signal processing, machine learning, and behavioral science can improve road safety by making people better drivers. I'll discuss several challenges in achieving this goal, as well as learnings from successful deployments in multiple countries. Interesting problems include inferring vehicular dynamics from noisy sensor data; accurate drive detection; detecting and discouraging distracted driving; designing good incentives for safe-driving; and the design of new sensing platforms to augment smartphone sensors.

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  • (2023)Graph neural networks for road safety modelingProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668387(52009-52032)Online publication date: 10-Dec-2023

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  1. Making Roads Safer by Making Drivers Better

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      cover image ACM Conferences
      MobiCom '17: Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking
      October 2017
      628 pages
      ISBN:9781450349161
      DOI:10.1145/3117811
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 04 October 2017

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      Author Tags

      1. behavioral science
      2. machine learning
      3. mobile sensing
      4. signal processing
      5. vehicle safety

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      MobiCom '17 Paper Acceptance Rate 35 of 186 submissions, 19%;
      Overall Acceptance Rate 440 of 2,972 submissions, 15%

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      Cited By

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      • (2023)Graph neural networks for road safety modelingProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668387(52009-52032)Online publication date: 10-Dec-2023

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