How telematics helps Grab to improve safety

Summary

Grab developed an in-house telematics engine to monitor driver-partner habits, detect crashes, and improve ride safety beyond passenger feedback. The mobile telematics SDK collects accelerometer, gyroscope, and GPS readings, running on-device processing for immediate accident detection while batching ride data for post-trip safety reports. Because triaxial sensor sampling rates vary independently across diverse devices, the SDK synchronizes data through interpolation to a uniform time grid followed by decimation to an output data rate. Telematics signals are transformed to the frequency domain using Fourier Transform for local compression before backend upload. Grab also combines telemetry with spatial data to identify dangerous road zones and plans to expand on-device event detection across all platform verticals.

Context

Relying purely on passenger feedback did not provide a definitive way to evaluate driver-partner safety, track route deviations, or detect vehicle accidents as the driver network scaled.

Approach / What changed

Grab built an in-house telematics engine and mobile SDK that collects triaxial accelerometer, gyroscope, and GPS data for on-device crash detection, synchronizes and compresses the sensor stream via Fourier Transform, and uploads the data post-ride for backend safety reporting and spatial analysis.

Takeaways

• Sensor data streams from independent accelerometer, gyroscope, and GPS units require interpolation to a uniform time grid followed by decimation to achieve consistent output sampling rates across varying mobile chipsets.
• Fourier Transform is applied to convert time-domain sensor signals into the frequency domain to compress telemetry files on the mobile device prior to backend upload.
• Driving telemetry evaluation requires distinct detection rules for two-wheeled versus four-wheeled vehicles and must account for hardware axis-bias caused by varying device orientations relative to the vehicle.

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