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ADAS, AV and AI Meetup — September 17

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Event artwork for ADAS, AV and AI Meetup — September 17

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What to expect

Explore how AI, machine learning and computer vision are advancing ADAS and autonomous vehicles at this virtual meetup on 17 September 2026, 9–11am PST.

Organised by the Amsterdam AI Machine Learning and Computer Vision Meetup, this two-hour online event brings together specialists from Porsche AG, Voxel51 and Dynamic Map Platform. Through three expert talks, attendees will examine the data, engineering and evaluation challenges involved in taking autonomous-driving AI from research into dependable real-world systems.

Talks and speakers

AI for Autonomous Driving: From Data to Decisions Tin Stribor Sohn, a PhD student at Porsche AG and Karlsruhe Institute of Technology and Tech Lead in data-driven engineering for autonomous driving, will discuss modern AI across the automated-driving development process. Expect practical perspectives on interpreting large-scale driving data, evaluating how AI systems behave on the road, where current approaches perform well and which difficult problems remain.

Advancing ADAS and Autonomous Vehicle Development with Multimodal Data Murilo Gustineli, Machine Learning Engineer at Voxel51, will explore how FiftyOne’s multimodal capabilities can combine camera, video, LiDAR, radar and other sensor data. The session covers unified workflows for visualisation, search, curation and evaluation, showing how teams can find challenging scenarios, investigate model failures and work towards safer autonomous systems.

From Survey-Grade Maps to Physical AI: Scaling Real-World Data for Training and Simulation Ryoto Miyake, Software Engineer at Dynamic Map Platform, will explain how survey-grade assets gathered across 1.8 million kilometres of roads are transformed into AI-ready resources. These include point clouds, imagery, HD maps, road-surface models and 3D Gaussian Splatting representations. The talk will address why geometric accuracy, semantic understanding and real-world diversity matter when structuring data for training, simulation and validation.

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