With the race towards L4+ vehicles, increased number of sensors per car, resolutions, frame rates, etc., the data produced by the systems grows exponentially. So are the costs.
Thus, efficient and cost-effective data handling becomes crucial. Existing infrastructure cannot cope with the demand.
Traditional lossless compression struggles with scalability due to limited speed and efficiency. While visually lossless algorithms compromise the reliability of ML models for the safety of connected and autonomous vehicle’s (CAV).
Jetraw Core is a high-efficiency low-latency 5-10:1 raw image compression integrated into certified environments, either on-board a vehicle system or in a data centre.
Jetraw applies lossless compression to the signal, following lossy compression to the noise. Thus it retains metrological properties of raw images while achieving higher ratio and speed than traditional lossless methods.
Unlike visually lossless codecs, it preserves raw quality and treats images as measurements, enabling reliable data-centric AI analysis and model training.
The large image data produced by Level 4-5 autonomous vehicles encounters slow upload/download speeds, and the rapidly expanding volume faces recurrent system capacity limitations.
The costs of cloud storage and access could run into millions of dollars. Jetraw Core addresses such issues and unlocks scalability for OEM and Tier 1 suppliers.
Cost of cloud storage
& network, USD/year
CO2 emissions,
tonnes/year
Time required
to transfer on 100Gbps
