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WeAreDevelopers LIVE • Nov 5, 2020

How Machine Learning is turning the Automotive Industry upside down

Jan Zawadzki

How do you process ten terabytes of edge vehicle data daily? Discover why the automotive industry's future depends on scalable, safety-critical machine learning architectures.

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#1 about 3 min

Introduction to machine learning in the automotive industry

Centralized artificial intelligence initiatives drive modern software development within the VW group.

#2 about 4 min

Economic footprint of the global automotive industry

The automotive sector generates massive global economic value while employing millions of people worldwide.

#3 about 2 min

Global car sales and the rising demand for mobility

Stagnating global car sales highlight a transitioning economic focus toward expanding future mobility solutions.

#4 about 3 min

How data growth powers machine learning capabilities

The exponential growth of generated data serves as the underlying engine for viable machine learning products.

#5 about 2 min

The virtuous cycle of machine learning in connected cars

Deploying intelligent products generates continuous user data to iteratively improve automotive algorithms.

#6 about 3 min

Automated driving and the project management triangle

Autonomous vehicles subvert traditional product constraints by simultaneously lowering operational costs and increasing travel quality.

#7 about 2 min

Enhancing user experience with intelligent car cockpits

Voice interaction and algorithmic personalization simplify software management inside modern vehicle interiors.

#8 about 3 min

Tackling data volume and sensor cost limitations

Massive volumes of generated vehicle data compound the ongoing challenge of expensive sensor integration.

#9 about 2 min

Managing the complexity of modern car software architectures

Orchestrating millions of lines of code across numerous embedded devices remains a structural hurdle for vehicles.

#10 about 3 min

Shifting to agile workflows for machine learning development

Transitioning toward data-driven, continuous monitoring paradigms replaces traditional waterfall methodologies for intelligent algorithms.

#11 about 4 min

Ensuring robustness and explainability in machine learning

Inherent biases in training datasets necessitate robust methods for interpreting decisions in safety-critical models.

#12 about 2 min

Summary of machine learning capabilities and engineering opportunities

The massive potential of artificial intelligence creates extensive engineering opportunities within modern automotive organizations.

Matching moments

2:52 min

Introduction to safety-critical machine learning in automotive contexts

Jan Zawadzki · WWC 2022

2:25 min

Navigating automotive complexity with AI runtime environments

Daniel Graff +1 · WWC 2021

2:07 min

Industrial applications of machine learning in autonomous vehicles

Alexandra Waldherr · LIVE

3:16 min

Driving developer productivity with AI in automotive tech

Mike Butcher Mike Butcher +3 · WWC 2024

6:28 min

Defining critical competencies for automotive AI engineering

Daniel Graff +1 · WWC 2021

7:31 min

Addressing participant questions on liability and machine learning

Georg Kühberger +1 · LIVE

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