Top experts took to the stage at AutoSens USA 2026 to explore the latest advancements, challenges, trends, and solutions within the ADAS and AV fields.
Recap the top sessions from Analog Devices, Stellantis, Waymo and more.
Prem Nayar
Managing Director – Automotive Video and Data solutions
Walking Toward Full Autonomy: The Evolution of Mobility Architecture
“Autonomy is not a switch that you flip”
In this keynote, Prem considered the development of human mobility and the parallels it offers to the evolution of vehicle architectures. Specifically, traced the progression from distributed, loosely coordinated control to increasingly centralized compute, and ultimately to hybrid approaches that combine high-performance central processing with intelligent sensing at the edge.
Analog Devices then examined what this transition enables, including faster response times, scalable software-defined capabilities, improved safety observability, and reduced power consumption, and discussed the implications for building the next generation of autonomous systems.
Catch the session on YouTube here
Emily Robb
Sr Fellow AI/AD Sensing, Processing, Localization
Scaling Perception: Architectural Realities from L2 to Higher Autonomy
The automotive industry is rapidly converging on centralized compute architectures and AI-driven perception stacks. However, scaling from L2 ADAS to higher levels of autonomy is not a linear exercise in adding compute or adopting new models.
This keynote examines how perception architectures, sensing strategies, and compute topologies evolve across levels of autonomy. It challenges the binary framing of centralized vs. distributed systems by identifying which functions naturally consolidate and which make sense to remain at the edge.
This talk connects sensing modalities, perception strategies, and interior sensing approaches with architectural design patterns against the backdrop of real world scalability constraints.
Catch the session on YouTube here
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Simon Verghese
Director of Sensors
Sensor Systems and Their Usage in Autonomous Vehicle Fleets
Lidars, radars, cameras, and microphones directly measure scene data that autonomous vehicles need for handling edge cases like fast-changing weather and complex urban scenes.
These sensing modalities degrade differently in various weather conditions, allowing for robust detection of other vehicles and vulnerable road users like pedestrians and cyclists.
Recent trends in ADAS are making available improved detectors and emitters that enable Level 4 sensing performance at low cost. We Simon also shares some examples and insights into the increasing role of AI in sensor processing and in understanding complex scenes.
Catch the session on YouTube here
Panel Discussion
Architecture Shifts: Collapsing ECUs for Scalable Sensing & Compute
This panel explores how and why OEMs and suppliers are collapsing distributed ECUs into unified sensing and compute platforms. What architectural trade-offs emerge around redundancy, safety certification, bandwidth, and fault isolation? How do these shifts impact sensor fusion strategies, software reuse, and over-the-air update models?
This discussion examines real-world deployment lessons, including where centralization delivers clear benefits, and where hybrid or transitional architectures still make sense.
From ADAS to higher-level autonomy, this session unpacks what scalable vehicle architectures really look like, and how today’s design decisions will shape performance, safety, and upgradability across vehicle lifecycles.
Catch the session on YouTube here
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Panel Discussion
Validating AI at Scale
The automotive industry is not immune to the AI revolution, and as AI becomes central to perception and decision-making, validating its performance at scale remains one of the most complex challenges facing OEMs.
Traditional validation methods struggle to keep pace with the non-deterministic nature of AI systems, the long tail of edge cases, and the sheer volume of scenarios required to build confidence in real-world deployment.
This panel brings together OEM experts for a deep, technical discussion on how AI validation is evolving in practice.
When failure occurs, how do engineers decode the origin of the issue? What tools, logging strategies, and observability frameworks are needed to make AI decision-making more interpretable and diagnosable? And what might be the impacts of compressed development cycles?
Catch the session on YouTube here
Interested in in-cabin monitoring technology?
With a pass to AutoSens Europe , you’ll also gain access to our co-located sister event, InCabin. Explore the InCabin agenda here >>