In this challenge, we will 

• Analyze the given dataset
• Identify patterns in given real-time data
• Use other relevant, supplementary data to explain patterns
• Learn, understand the insights and build a model if necessary.
• If required, use explainable AI to understand model predictions.

Technologies we will use

• Regression
• Time series analysis
• Exploratory data analysis
• Data science / Statistical analysis 

‍Who are we looking for? 

Students in Computer science, AI/ML enthusiasts, transportation engineers, researchers at universities. Undergraduate/Graduate or higher.

You will collaborate with a diverse team of up to 50 international collaborators in subteams. You can join as a contributor (8-12 hours per week commitment for 8 weeks) or coach (2-4 hours per week, only for experienced ML professionals)

We’ll organize a masterclass on using the dataset provided by iRaaste for the challenge.

Dataset description‍

The proposed dataset is a pseudo-anonymized real-time data from vehicles on roads. The dataset consists of events from. vehicles about possible collision alerts and driver monitoring. The dataset consists of more than 10 types of alert events with over 1000 data points with location and time stamps.

Quick facts about the problem

We at INAI have been involved in the iRASTE projects, aimed at improving road-safety through AI-based collision avoidance systems.

INAI has been instrumental in deploying the largest ADAS Pilot in India with over 450 vehicles plying in Nagpur city, and on the National highways of Telangana.

Did you know

🚗 INAI has been instrumental in deploying the largest ADAS Pilot in India with over 450 vehicles plying in Nagpur city, and on the National highways of Telangana.

🚕 The Government of India is aiming for a 50% reduction in road fatalities on Indian roads by 2030 that paves the path towards VISION ZERO.

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