Rivian uses machine learning for predictive vehicle maintenance and ADAS
RivianAgent Bricks · Databricks SQL · Delta Lake +1
Rivian, an electric vehicle manufacturer, uses machine learning on the Databricks Data + AI Platform to analyze IoT sensor and telemetry data from over 70,000 Electric Adventure Vehicles to power predictive maintenance, addressing potential issues before they affect drivers. Its ADAS team processes telemetric accelerometer data (pitch, roll, speed, suspension, airbag activity) to improve smart driving features like adaptive cruise control and collision warnings. Rivian is also using machine learning to optimize battery efficiency in colder temperatures and improve autonomous driving system accuracy. The platform delivered a 30-50% increase in runtime performance and grew from 250 to 1,000+ platform users in one year.
Overview
Rivian, an electric vehicle manufacturer, uses machine learning on the Databricks Data + AI Platform to analyze IoT sensor and telemetry data from over 70,000 Electric Adventure Vehicles to power predictive maintenance, addressing potential issues before they affect drivers. Its ADAS team processes telemetric accelerometer data (pitch, roll, speed, suspension, airbag activity) to improve smart driving features like adaptive cruise control and collision warnings. Rivian is also using machine learning to optimize battery efficiency in colder temperatures and improve autonomous driving system accuracy. The platform delivered a 30-50% increase in runtime performance and grew from 250 to 1,000+ platform users in one year.
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Inspect the highlighted sourceThe challenge
Before shipping its first EAV, Rivian's legacy cloud tooling required 30 to 50 large, operationally complex compute clusters with frequent outages, forcing teams into troubleshooting over analysis, while disjointed systems and required data-language expertise created data silos and limited non-technical users' access to data.
The solution
Rivian modernized its data infrastructure on the Databricks Data + AI Platform to unify data for downstream analytics and machine learning, using Unity Catalog for centralized governance and access control, so its ADAS team can process telemetric accelerometer data (pitch, roll, speed, suspension, airbag activity) to power predictive maintenance and improve smart driving features, plus optimize battery efficiency in colder temperatures and improve autonomous driving system accuracy.
Reported business value
Rivian saw a 30%-50% increase in runtime performance and grew Databricks platform users from 250 to over 1,000 in a single year, a 75% increase.
Sources
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