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World Congress 2022 • Jun 15, 2022

What non-automotive Machine Learning projects can learn from automotive Machine Learning projects

Jan Zawadzki

Prepare your machine learning pipelines for strict regulations like the EU AI Act. Learn how adopting rigorous autonomous driving standards keeps your models traceable, robust, and failure-proof.

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#1 about 3 min

Introduction to safety-critical machine learning in automotive contexts

How statistical models safely execute high-speed decisions in vehicles.

#2 about 4 min

Market growth and corporate investment trends in artificial intelligence

Why exponential data generation drives significant enterprise investment in machine learning.

#3 about 4 min

Regulatory compliance and the upcoming European Union AI Act

How new legislation categorizes risks for both embedded systems and standard recommendations.

#4 about 5 min

Digitizing the vehicle of the future at Volkswagen Group

How consumer demand, climate crisis, and social challenges reshape automotive engineering.

#5 about 5 min

Decoupling hardware and software components through standardized platform architecture

Replacing supplier black boxes with unified operating systems to improve agility.

#6 about 3 min

Creating virtual feedback cycles for in-car artificial intelligence platforms

Leveraging vehicle fleets to continuously capture operational data for model improvement.

#7 about 3 min

Perception layers and trajectory planning in autonomous driving systems

Translating raw sensor inputs into actionable movement commands using statistical models.

#8 about 2 min

Function safety assurance and operational design domain modeling concepts

Defining specific environmental boundaries to guarantee expected vehicle performance for statistical safety models.

#9 about 3 min

Adapting traditional waterfall development methodologies for machine learning teams

Why rigid requirements gathering remains necessary before writing any data science code.

#10 about 2 min

Separating dataset creation from low-level software implementation steps

Breaking apart standard linear development to allow faster iterative training loops.

#11 about 2 min

Establishing comprehensive safety assurance cases for operational machine learning

Measuring relevant failure points and ensuring system redundancy when neural networks fail.

#12 about 4 min

Managing machine learning datasets as distinct internal engineering products

Setting concrete requirements and continuous tracking for data collection and testing.

#13 about 3 min

Improving computer vision resilience using augmentation and out-of-distribution sampling

Combining multiple synthetic image alterations to reduce sensory data drift issues.

#14 about 3 min

Scaling algorithm functionality between platforms with delta learning techniques

Adjusting existing models for new target domains without requiring complete retraining.

#15 about 8 min

Audience questions on data privacy, sustainability, and market hype

Clarifying anonymous collection methods, carbon neutral goals, and realistic development expectations.

Matching moments

2:21 min

Introduction to machine learning in the automotive industry

Jan Zawadzki · LIVE

4:55 min

Audience Q&A on autonomous driving models and data

Liang Yu · WWC 2022

2:25 min

Navigating automotive complexity with AI runtime environments

Daniel Graff +1 · WWC 2021

7:31 min

Addressing participant questions on liability and machine learning

Georg Kühberger +1 · LIVE

1:29 min

The virtuous cycle of machine learning in connected cars

Jan Zawadzki · LIVE

2:07 min

Industrial applications of machine learning in autonomous vehicles

Alexandra Waldherr · LIVE

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