Project Overview
The primary objective of the project was to develop AI applications that are aimed at insurance providers, fleet companies, and drivers, using IoT mobility data captured by the client’s devices. The solution also needed to be deployed on AWS Cloud Infrastructure in order to ensure scalability and real-time performance.
Scope:
- Build a serverless data pipeline to store, process, and analyze large amounts of mobility data.
- Build real-time analytics on the data lake for quick oversight of data.
- Turn raw data into structured and clean data that can be easily stored in data warehouse.
Key Challenges
-
Unique Application Development The required applications were new ideas with no existing references, which made the development of complex algorithms from scratch a necessary requirement.
-
Complexity of Models The project involved creating algorithms that inferred various parameters using statistical models and AI techniques. These algorithms are needed to process and analyze vast, unstructured data from different sources, such as sensor data, video feeds, etc.
Our Solution
Car Accident Recreation
- This application uses sensor data and accident video footage to find the cause of an accident. It considers various factors, such as impact severity, traffic signals, traffic violations, and driving direction.
- The application is used by both drivers and insurance companies to validate claims, offering an objective recreation of the accident event
Driver Behaviour Analysis
- This application calculates a driver behaviour score using sensor data, weather conditions, speed trends, and driving patterns. It helps in identifying risky driving behaviour.
- The G-Sensor data (acceleration and deceleration data) was used to improve the sudden alert algorithm, which raises alerts for hard acceleration and harsh braking events.
- This tool is particularly useful for fleet companies and insurance providers to monitor and assess driving behaviour.
Key Results
Accident Recreation Application
The application provides precise on-site analysis of an accident, allowing insurance companies to reconstruct accidents based on objective data (sensor and video footage) rather than relying solely on statements of the driver.
It also enables digital damage assessment, making the claims validation process faster and more accurate and reliable.
Driver Behaviour Analysis Application
The driver behaviour analysis tool allows fleet companies to categorize drivers into high- and low-risk groups. This enables companies to proactively address risky driving behaviour, leading to reduced operational risks and potential lowering of the number of future accidents.
Fleet companies can use this data to reduce insurance costs by identifying and correcting dangerous driving behaviours early on.
Latest Insights
Explore In-Depth Insights
and Industry Trends
How Does AI in Retail Drive 2X Return on Investment?
AI in retail drives 2X return on investment by helping retailers earn 20% additional revenue from their businesses.
How AI Drives Digital Transformation in Retail?
Digital transformation in retail uses several AI technologies such as predictive analytics, computer vision, cloud computing, AR, VR, IoT, robotics, and headless e-commerce.
11 Ways of Using Generative AI to Improve Transportation Safety And Compliance
Generative AI improves transportation safety and compliance by predicting and preventing risks and streamlining compliance for the transportation industry.
Data Analytics in Transportation: Use Cases and Benefits
Data analytics in transportation makes organizations more efficient, safe, and cost-effective.
