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From Tragedy to Technology: Accelerating Safer Roads Through AI at Scale
SessionData Analytics Part 3
DescriptionOver a million people die in traffic collisions each year—making autonomous driving not just a technical challenge, but a humanitarian imperative. Advanced Driver Assistance Systems (ADAS) and self-driving systems powered by AI are advancing rapidly, but traditional architectures create massive inefficiencies. Each stage of the AI lifecycle—from data ingestion to simulation and training—often replicates 100+ PB of data multiple times, leading to petabyte-scale duplication, delays, and millions in wasted infrastructure. By unifying the entire physical AI pipeline—including HiL, SiL, LiDAR, telemetry, FMV, CAN-Bus, and neural network training—into a single data platform, innovators can eliminate redundancy and accelerate delivery. This approach supports millions of GPU cores running real-world simulations across thousands of virtual vehicle models, with seamless access to data across global sites. It reduces provisioning times from weeks to minutes and saves $50–$100M per program—enabling faster validation and dramatically accelerating the path to safer roads through scalable, real-time AI.
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