Table of Contents
AI-enabled vehicles & eSports
Implementation of a core real-time processing analytics engine to enable a platform approach
Type of data
- WebSocket Telemetry
- IoT data
- Simulators data
Data Architects
- Unified batch and streaming engine, enabling advanced data transformations with low latency, and the ability to easily implement User-Defined Functions (UDFs) in Python at scale
- Capacity to process millions of data points per second
- Reconcile streaming data errors (late data, sampling frequency mismatches, multi-time series alignment)
- Full platform approach, with a possibility of a front-end integration downstream
- Supporting applications that involve high-frequency telemetry data streaming and advanced data transformations.
Signal Processing Engineers
- Design User-Defined Functions for Remote Monitoring and Control: receive real-time telemetry data, including GPS location, speed, sensor readings to generate data-driven insights.
- Fleet Management: monitoring vehicle status, routing, fuel efficiency, and maintenance needs in real time.
- Empower Engineers: Transmit real-time data about mechanical and electrical systems, to facilitate diagnosis and prevention of mechanical failures, enabling predictive maintenance.
End Users: Drivers and eDrivers
- Consuming dynamic dashboards for various car and performance metrics. Users receive immediate updates without the need for manual refresh.
- Real-time alerts and status updates: ensuring drivers can adapt to changing road conditions and other variables.
- Real-time game updates and analytics, while ensuring low latency and an immersive gaming experience.