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14. – 16. April 2026 | Frankfurt/Hanau
Auto[nom]Mobil
15. – 16. April 2026
Mittwoch, 15.04.2026
Introduction Talk
Dr.-Ing. Michael Fausten - Robert Bosch GmbH; Prof. Dr.-Ing. Thomas Helmer - Technische Hochschule Ingolstadt
Commercial road autonomy has gained significant momentum both in terms of geographic footprint and number of trips. How will this evolve?
Safety performance, as reported by operators, is promising. What are key approaches?
For robotaxis, Waymo is the dominant player, while several other companies have also transitioned to commercial operations. Which companies can provide significant competition?
Autonomous trucking is seeing extensive development, testing, and deployment across private grounds, streets, and highways. 2025 saw the advent of driverless highway trucking but only at a small scale; 2026 will see a spike in driverless operations.
The role of OEMs is quite strong for trucking, whereas passcar OEMs are in the background for robotaxis.
Introducing the SYNERGIES Scenario Dataspace: a federated, interoperable European repository enabling access to real and synthetic driving scenarios for CCAM development, testing, and validation.
From European datasets to harmonized metadata: how SYNERGIES ensures semantic, pragmatic, and technical interoperability across diverse scenario sources and tools.
Integration of scenario generation, qualification, and governance: presenting a unified framework linking physical testing, simulation, AI-based scenario creation, and safety assurance needs.
Synergies with ongoing International Initiatives and alignment with the European strategy for data spaces and CCAM safety validation.
Expected impact for industry and regulators: enabling scalable scenario-based validation across OEMs, suppliers, technical services, and research organizations.
European SCART-Institute: Scenario-based Type Approval Processes in Europe – Current Status and Outlook
New AI‑enhanced functions such as 3D body‑pose detection enabling NCAP‑relevant out‑of‑position assessment (e.g., feet on the dashboard, head near the airbag) through large‑scale auto‑labelling and improved model design
AI model applied directly to radar spectral data that clearly outperforms classical signal‑processing approaches
Vital‑sign monitoring with a focus on heart‑rate estimation to detect medical anomalies before driver incapacitation
Outlook on in‑cabin sensor fusion and the integration of interior and exterior sensing
In-Cabin Sensing Technologies Transforming Safety, Comfort, and Regulatory Compliance In Modern Vehicle
For decades, automotive radar has been limited by a rigid processing pipeline that forces complex sensor data into a sparse point cloud, creating an irreversible information bottleneck. Today Bosch is pioneering a new paradigm with "AI Spectrum Radar" (AISR), moving beyond single-purpose models to develop a versatile and large radar model. Our approach is built on three strategic pillars. First, we feed raw radar spectrum data directly into our models, preserving the full richness of the sensor's information. Second, we employ powerful Transformer-based architectures capable of learning a deep, contextual understanding of this data. Instead of training for one specific task, we use tasks like 3D occupancy as a dense, self-supervised pre-training objective. This teaches the model a "physics of radar" understanding that is broadly applicable. The power of this pre-trained foundation model is then demonstrated by its ability to solve downstream tasks, such as semantic segmentation, object detection, or BEV free-space estimation, with superior performance. We will showcase results from our live vehicle demonstration, supporting the immense potential of the approach.
Transition from human-driven to highly-automated transport in the long term
Vehicle cooperation and communication with humans, automation and other road users
What are effects of human-technology migration of automated traffic systems on the safety and efficiency of road traffic as well as on usability and acceptance?
Attempts to design migration-capable vehicle automation systems. How can the industry support Human Systems Migration? Is standardization the way to go?
