Self-driving startup Drive.ai Acquired By Apple
19 days ago
How Hackathon Winners Apply Machine Learning to Minimize Rash Driving
- Nodejs - To create the API server and the mock sensor data generator
- Kafka - To build the data pipeline
- Apache Spark - To process the real-time data stream and generate metrics to measure the driving quality
- ReactJs - To create the dashboard web app
- Google roads & maps API: To get the traffic and ETA data
- API Server: This matches the data with the schema and if valid, it puts the data in Kafka queue.
- Engine: Made with the Apache Spark, this helps sensor data to aggregate and form metrics such as sharp acceleration, hard braking, sharp turns, etc. These metrics, in turn, are used to generate a dynamic driving quality score for the driver. This score forms the basis for a lot of analytics and functionalities that this system provides.
- Dashboard: The dashboard provides a beautiful and intuitive interface to take proactive decisions as well as run analytics using the provided APIs. It has been written using ReactJS.
- A dynamic profile and the dashboard of the rider describing his driving style, which affects his rating.
- An actionable "real-time" rash driving reporting system which allows the authorities and the hub in charges to react before it's too late.
- A dashboard usable by both the fleet managers and traffic police control board to visualize the data such as incident distribution by time, which tells at what time of the day a driver is more likely to drive in an unsafe manner.
- A modular system in which the new data sources, metrics, and models can be added so that the third-party vendors can be easily on-boarded onto the platform.
- Setting up the entire system architecture with different components by developing them in isolation and then combining them together to work seamlessly
- Deciding the thresholds for different metrics after which the driving will be considered rash
- Creating a linear predictor for the driving quality score vs time with only one data point
- Creating a synthetic feature as generating the score itself is challenging enough
- Create an SDK for easy data collection and integration with different apps and make it possible for third-party vendors to utilize this data
- Improve the driving score model to include even more parameters and make it more real-world oriented
- Create a social profile which lets the users share their driving score
- Enable enterprise-grade plug-n-play integration support