Aravo Solutions > Case Studies > Accelerating Autonomous Vehicle Development with IoT

Accelerating Autonomous Vehicle Development with IoT

Technology Category

Platform as a Service (PaaS) - Application Development Platforms
Robots - Autonomous Guided Vehicles (AGV)

Applicable Industries

Automotive
Cement

Applicable Functions

Product Research & Development

Use Cases

Autonomous Transport Systems
Vehicle Performance Monitoring

About The Customer

Incite's customers are some of the world's largest automakers who are working on developing safer autonomous vehicles. These automakers rely on the Rapid OEM Automotive Data (ROAD) platform provided by Incite to process the massive amounts of unstructured data generated by their test fleets. This data is crucial for the development and deployment of autonomous driving features. The customers were previously facing challenges in accessing and analyzing the data they needed, which was slowing down their product development. They had to manually search individual files for data, and the ingestion of data into end-user dashboards took weeks.

The Challenge

Incite, a company that provides the Rapid OEM Automotive Data (ROAD) platform to the world's largest automakers, was facing significant challenges in processing the massive amounts of unstructured data generated by test fleets. This data is crucial for building safer autonomous vehicles. However, frequent performance issues made it difficult for Incite to deliver fleet behavior metrics that clients could easily consume. Additionally, customers had to spend hours manually searching individual files for the data they needed, which significantly slowed down product development. The process of ingesting terabytes of data and making it visible in end-user dashboards took two to three weeks, which was a major bottleneck in the development process.

The Solution

To overcome these challenges, Incite integrated the Databricks Lakehouse Platform into its ROAD platform. This platform processes hundreds of terabytes of data points and scales to support teams working on capabilities such as self-driving on highways, preventing parking lot collisions, and navigating neighborhood streets. With this integration, Incite can now ingest terabytes of data and make it visible in end-user dashboards in less than 12 hours. Furthermore, with Databricks SQL, Incite’s clients can perform structured and ad hoc analysis to explore host vehicle and feature event behavior across many dimensions of vehicles, driving scenarios, vehicle software versions, and driving conditions. To correct bad client data, Incite uses Databricks Delta Lake, which saves time compared to performing rollbacks. Incite is also working with Databricks to develop stronger geospatial capabilities that will help detect anomalies in clients’ software and prevent car crashes.

Operational Impact

The integration of the Databricks Lakehouse Platform into Incite's ROAD platform has significantly improved the operational efficiency of Incite and its clients. The ability to ingest and make data visible in end-user dashboards in less than 12 hours has accelerated the development process. The use of Databricks SQL for structured and ad hoc analysis has made it easier for clients to explore and understand vehicle and feature event behavior. The use of Databricks Delta Lake for correcting bad client data has saved time compared to performing rollbacks. The development of stronger geospatial capabilities will further enhance the ability to detect anomalies in clients’ software and prevent car crashes. Overall, the solution has not only improved the speed and efficiency of data processing but also the quality and safety of autonomous vehicle technology.

Quantitative Benefit