Electric vehicle battery data analytics
AVL’s Gerhard Schagerl and Alwin Tuschkan explain how to use electric vehicle battery data analytics to support design decisions, optimize operation, and predict issues in the field.
The adaptive battery digital twin: how to maximize the use of a BMS application layer with cloud connectivity
Stéphane Turlier of Electra Vehicles and NXP’s Maciek Krawczyk discuss current and future considerations for EV battery packs and management systems.
Increase productivity with machine learning for intelligent online monitoring
AVL experts explain how to use machine learning to intelligently monitor the health of the unit under test and the overall system.
Engineering data science – driving the future of mobility
FEV’s Dr Umesh N. Hivarkar explains the role engineering data science will play in shaping the future of mobility.
Making artificial intelligence and machine learning safe
NXP’s Andres Barrilado explains how AI and ML will make their way into automotive safety functions as we consider how to avoid systematic failures and define new problem statements around inference to provide safety-related functions.
Exploring behaviour AI: automotive applications
Humanising Autonomy’s Raunaq Bose and Pier Paolo Porta of semiconductor design company Ambarella discuss AI applications for the automotive and urban mobility industries.
Winter is coming: how AI predicts slippery road conditions and enables automated driving
Experts from Continental, IBM, and Zeiss present a general introduction to AI and explain how to leverage it to predict upcoming road conditions.
Expediting e-powertrain durability testing with artificial intelligence
AVL’s Matthieu Clauet and Hongbo Sun explain customer pain points during the durability testing process and how to achieve optimum validation and development results using AI.
AI-based fast dynamic DoE methodology for motorcycle race engine performance optimization
Experts from AVL and Ducati discuss the advanced methodologies, tools, and facilities that helped optimize the engine performance of the motorcycle that won the 2020 MotoGP Constructors World Championship.
Accelerating automotive aftersales diagnosis and repair by leveraging artificial intelligence
Experts from SOMIT Solutions and KPIT discuss an AI-based diagnostics solution, which seamlessly integrates root-cause diagnosis with the corresponding repair/service information.
How to level up your test drive experience: an efficient approach using existing test resources
b-plus automotive experts explain how software-based planning and AI-based data evaluation of test drives can make highly-efficient use of existing test resources possible.
Accelerating the development of augmented reality lane-level guidance: fusing HD-enriched maps with 6D environmental data
NNG’s Martin Pfeifle, Christoph Bonik of Artisense, and atlatec’s Henning Lategahn discuss their collaboration on augmented reality lane-level guidance.
How automotive augmented reality is “Changing The Way We See”!
Elektrobit’s Nazareth Samson presents a bird’s-eye view of augmented reality and explains its role in the future of mobility.
Data annotation – how AI learns from humans
Experts from HERE Technologies provide unique insight into data annotation, the process of humans labelling static and dynamic imagery so machines can understand what they are ‘seeing’.
What will drivers do with all this car camera vision?
Nexar’s Roni Floman explains how cameras will impact the vehicle market and what that means for OEMs and their suppliers.
The transformational impact autonomous driving will have on cars and the transportation system
Qualcomm’s Riccardo Calabro discusses the auto industry’s digital transformation and explains how 5G will be the connectivity fabric for future vehicles.
Servitization opportunities for automakers with a future-ready service delivery platform
KPIT’s Omkar Panse outlines how automakers can take advantage of the revenue growth opportunities offered by data-driven services.
Building the road to the future with training data for autonomous vehicles
Appen experts look at uses and types of training data, case studies for different data types, and the best ways to future proof your data to ensure great mileage for your AI projects.
How to improve data accessibility for efficient autonomous vehicle development
Mark Pfeiffer, CTO and Co-Founder of SiaSearch, discusses how data recordings can be made easily accessible through content-based search.
How to use artificial intelligence to automate vehicle user interface tests
Borislav Nazarski, Elektrobit’s testing and validation expert, explains how to overcome the challenges involved in automating UI tests.