Liang Yu
Finding the unknown unknowns: intelligent data collection for autonomous driving development
#1about 1 minute
Finding the unknown unknowns in autonomous driving
The primary challenge in autonomous driving is identifying and collecting data on rare, anomalous scenarios that models are not trained to handle.
#2about 1 minute
Introducing Cariad and its unified software platform
Cariad, a Volkswagen subsidiary, is building a unified software and tech stack to accelerate innovation for all Volkswagen group brands.
#3about 2 minutes
The Big Loop system for intelligent data collection
The Big Loop system solves the high cost of traditional data collection by intelligently aggregating only useful information using dedicated hardware.
#4about 3 minutes
Understanding the long-tail problem in driving scenarios
The long-tail problem refers to rare but critical events, like noisy sensor data or unknown objects, that can be identified using methods like uncertainty estimation.
#5about 3 minutes
How INSTINCT software identifies valuable data
The INSTINCT software uses deep neural networks to analyze sensor data in real-time and calculate an uncertainty score to flag challenging scenarios for collection.
#6about 3 minutes
The complete data-driven development cycle in action
The Big Loop enables a continuous cycle of driving, uploading valuable data, labeling, retraining models, and deploying them back to vehicles via over-the-air updates.
#7about 1 minute
Scaling data collection with the pioneering fleet
The Big Loop technology is being deployed in a retrofitted "pioneering fleet" to scale data collection before its full rollout in millions of future vehicles.
#8about 5 minutes
Q&A on ethics, model deployment, and regional data
The discussion covers ethical dilemmas, local vs cloud model execution, setting dynamic uncertainty thresholds, and the goal of creating globally applicable models.
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