Machine learning to save lives on the road
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.
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!
– Handling 3 phases
– Ensemble of ensembles
- Roller coasters, bumping into bins, skydiving!
Data - More and more and more
- Customer feedback
- Manual review - label noise
Algorithm - sophistication
- Physics + Data + Machine learning
- Largest repository of collision data
- Most widely used smartphone based algorithm
- Deep learning
Looking back - what have we learnt?
[The sequence appears in the talk attached below (timestamp from 6 min to 27 mins)]
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.