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WeAreDevelopers LIVE • Mar 23, 2022

Intelligent Data Selection for Continual Learning of AI Functions

Nico Schmidt

Stop wasting bandwidth on random fleet data. Learn how intelligent edge filtering isolates low-confidence edge cases, drastically reducing overhead while accelerating autonomous AI.

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#1 about 4 min

Introduction to active learning and continuous data selection

The core use cases and definitions behind actively selecting diverse datasets for machine learning functions.

#2 about 4 min

Evaluating edge data sources and automotive compute capabilities

The constraints and benefits of using static data lakes versus real-time testing and customer vehicle fleets.

#3 about 3 min

Capturing informative data targets from the long tail

How targeting rare traffic scenarios, unusual sensor noise, and underrepresented classes improves model accuracy.

#4 about 4 min

Methodologies for mapping prediction uncertainty and structural anomalies

Techniques for estimating prediction certainty and detecting new operational density patterns.

#5 about 2 min

Utilizing softmax uncertainty for vehicular traffic light detection

Using native score aggregations to capture bounding box errors and weak signal situations.

#6 about 5 min

Benchmarking active learning algorithms against random data sampling

Why evaluating selective triggering demands customized corner-case test datasets beyond standard baseline measurements.

#7 about 1 min

Developing verifiable safety metrics for automated driving perception

Defining standardized performance metrics to assure the reliable safety operations of automotive perception algorithms.

#8 about 4 min

Deploying intelligent edge filters to onboard vehicular modules

Using the in-car instinct module to filter high-value detection snippets for cloud upload before refining production models.

#9 about 3 min

Adapting vehicular perception models to new geographical domains

Applying data learning concepts and targeted upload triggers to bridge environmental gaps between regional deployments.

#10 about 3 min

Architecting a universal plugin framework for data selection

Constructing an independent module topology that accepts logical, diversity, and novelty-based algorithms.

#11 about 2 min

Exporting framework independent representations for edge processing platforms

Exporting distinct training models to neutral ONNX formats and accelerating inference pipelines to run across disparate edge processing units.

#12 about 2 min

Bridging Python prototyping environments and embedded systems languages

Overcoming prototyping barriers by optimizing zero-copy and inter-process communications for efficient onboard execution.

#13 about 4 min

Navigating software integration compliance in safety automotive environments

Implementing open source license validations, strict code quality benchmarks, and complete requirements traceability.

#14 about 3 min

Decoupling cloud workflows from specific machine learning frameworks

Emphasizing agnostic pipelines that support mixed stacks without enforcing rigid cloud dependencies.

#15 about 3 min

Gauging specific model improvement metrics through custom situations

Examining performance distributions relative to data selection approaches and evaluating dataset composition bounds.

#16 about 5 min

Monitoring production regression using side loaded analysis logic

Establishing metrics alongside edge production systems to verify that machine learning inputs do not drift.

#17 about 4 min

Balancing data science skillings alongside systems engineering rigor

Evaluating candidate profiles for rigorous systems engineering aptitude within active data operations.

#18 about 3 min

Incorporating automated machine learning optimizations into hardware loops

Weighing the cost benefits of architectural searches against the constraints of embedded automotive microcontrollers.

#19 about 2 min

Distinguishing extreme system outliers from boundary line predictions

Why isolating unexpected system events requires merging diversity scoring frameworks with raw uncertainty logic.

Matching moments

2:52 min

Introduction to safety-critical machine learning in automotive contexts

Jan Zawadzki · World Congress 2022

4:55 min

Audience Q&A on autonomous driving models and data

Liang Yu · World Congress 2022

2:25 min

Navigating automotive complexity with AI runtime environments

Daniel Graff +1 · World Congress 2021

3:37 min

Transitioning automated driving to neural networks and model-based perception

Katrin Lehmann Katrin Lehmann +1 · World Congress 2025

1:29 min

The virtuous cycle of machine learning in connected cars

Jan Zawadzki · LIVE

1:43 min

Optimizing AI model execution for in-car inference

Daniel Graff +1 · World Congress 2021

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September 25, 2026 · 11:40–12:10

Stage 9

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September 25, 2026 · 12:55–13:25

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Closing the Visibility Gap: Lessons from Safety Critical Agentic Systems

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Frontier AI Lead at Turing

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