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Aditya Karnik

@adityakarnik

Machine learning to save lives on the road

Submitted Jun 28, 2019

Session type: Full talk of 40 mins Session type: Full talk of 40 mins

Every year over 1.3M people die on roads. In recent years the rates of fatality and collisions have increasingly gone upward, reversing a several decade long downward trend.

At Zendrive, we use smartphone data to understand and decode unsafe driving behaviours like aggression, non- adherence to the rules of the road and distraction. Using sophisticated machine learning techniques and massive amounts of data (150B miles of data over 50M users), we have built the world’s leading driving behaviour analysis platform that has already helped save hundreds of lives.

A core component of this platform is an algorithm to detect vehicular collisions. In this talk, we aim to take you on the fascinating journey of building this algorithm through myriads of challenges - smartphone sensors, data acquisition, detection of rare events, testing, and so on. The talk will highlight how these challenges were overcome through a combination of creative problem-solving and sophisticated ML techniques.

Outline

Why have a collision detection algorithm?

  • Saving lives by speeding up emergency response
  • Measure of risk on the road

High level challenges

  • Rare event (1 per million miles)
  • Mix of time-scales
  • Smartphones - no custom hardware

Building an MVP

  • Where is the data?
    -- OEMs, Being creative with misuse!
  • Algorithm
    -- Handling 3 phases
    -- Ensemble of ensembles

Curious cases

  • Roller coasters, bumping into bins, skydiving!
    -Continuous improvements

Data - More and more and more

  • Customer feedback
  • Manual review - label noise

Algorithm - sophistication

  • Physics + Data + Machine learning

Standing today

  • Largest repository of collision data
  • Most widely used smartphone based algorithm

Looking ahead

  • Deep learning

Looking back - what have we learnt?

[The sequence appears in the talk attached below (timestamp from 6 min to 27 mins)]

Speaker bio

Aditya Karnik is Director of Data Science at Zendrive. He has 14+ years of experience in academic and industrial research labs. His interests are in Mathematical modeling, Optimization & control and Predictive modeling.

Slides

https://drive.google.com/file/d/1tMWf_fa3Oe5_DOFTWa07lh_iV2oaOgJo/view?usp=sharing

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Comments

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  • AB

    Abhishek Balaji

    @booleanbalaji

    In addition to the feedback above:

    • The talk looks interesting, based on the video shared of the talk at IISc
    • More technical details are needed on how the algorithms were tweaked (specifically, reduction of false positive/negatives) and at least some of the key insights in how to deal with problems with very high class imbalance but where the decision is very critical
    • Also, the talk mentions using the accelerometer and gyroscope in the phones to infer collisions. It would help if more details of how the signals from these device components are processed and combined to arrive at a final probability

    We need these points incorporated into the slides and submitted asap to continue evaluation.

    Posted 5 years ago
  • AB

    Abhishek Balaji

    @booleanbalaji

    Hi Aditya,

    We're evaluating this proposal for the conference. Add the slides ASAP. Also share a link to any previous talk you've presented in the proposal.

    Your slides should cover the following:

    • Problem statement/context, which the audience can relate to and understand. The problem statement has to be a problem (based on this context) that can be generalized for all.
    • What were the tools/frameworks available in the market to solve this problem? How did you evaluate these, and what metrics did you use for the evaluation? Why did you pick the option that you did?
    • Explain how the situation was before the solution you picked/built and how it changed after implementing the solution you picked and built? Show before-after scenario comparisons & metrics.
    • What compromises/trade-offs did you have to make in this process?
    • What is the one takeaway that you want participants to go back with at the end of this talk? What is it that participants should learn/be cautious about when solving similar problems?
    Posted 5 years ago
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