Predicted by 2030 there will be 90 million autonomous vehicles on the road, creating 1 ZB of data. Sophisticated models built in the cloud need fast access to this data. Data models may need to be rebuilt in the case of errors or accidents. Multi-cloud provides flexibility to leverage the best-of-breed tools to rapidly re-calculate and release these models in the event of unforeseen accidents or errors in the vehicles.
ACCELERATE PATTERN RECOGNITION WITH AI
Analyze huge datasets to train connected cars how to reach to changing road conditions, identify and avoid people and obstacles in the road. Learn from post-crash data uploaded by cars in service to prevent recurrence.
Services like AWS IOT Core can securely support billions of connected devices and enable them to interact securely with cloud applications including AWS Lambda and Amazon Kinesis.
COST-EFFECTIVE DATA LAKES
Native cloud storage solutions are too expensive and may not have the right performance levels for the exponential growth of sensor data, images, and maps used and reported by each vehicle. Faction CCVs offer multiple storage tiers to balance performance requirements, cloud-adjacency, and budgets.
Public clouds like Microsoft Azure are working with manufacturers to provide over-the-air updates that deliver navigation intelligence (weather, traffic, infrastructure), in-vehicle infotainment (IVI) and voice assistants to improve market competitiveness.