AI in Automotive Podcast · Jayesh Jagasia

AI in Automotive - #405 - Andrew Fleury, CEO Luna Systems and Chris Tingley, CEO EVWare

·49 min·2 clips

And just to think that, you know, the way they have envisioned the future is an extrapolation or an extension of the present, probably not factoring in some serious disruption in terms of how people are going to live.

The season begins with cities and the pressure they are under. It treats urban growth as central to civilization and asks where AI fits in. Andrew Fleury explains Luna Systems' work with computer vision for micromobility scooters. Cameras on scooters and AI analysis help operators understand rider safety and behavior. This is less about watching people and more about seeing where trouble starts. The episode keeps returning to the quality of city data. Cities have generated huge amounts of it for years, but much of it was hard to use. AI and cloud infrastructure are presented as the shift that makes that data more actionable. That shift can compress decision time by orders of magnitude. Once the data is organized properly, patterns start to show. Some issues cluster into hotspots. Those hotspots can point to sidewalk riding, bad circulation, or design choices that do not work for every mode of movement. The episode treats that as a planning problem, not just a compliance problem. A recurring theme is that cities often change infrastructure for cars first. Other users of the street are left to adapt afterward. The Grenoble example makes that concrete. A corner was pedestrianized to stop cars from cutting through. The change worked for the intended use. It also became a major hotspot for sidewalk riding. The episode uses that case to show how one intervention can create a new friction point elsewhere. Mobility systems are connected, whether planners want them to be or not. The discussion also draws a line around expertise. The Luna team has deep computer vision knowledge, but not urban planning credentials. That matters, because data can point to a problem without deciding the policy answer. The city still has to choose what to build, paint, or redesign. The show returns several times to collaboration. Companies like Luna Systems and EBWare are framed as part of a larger ecosystem. Their role is to help cities make smarter, safer, and more sustainable decisions. The closing movement widens the lens again. It argues that the rise of AI and cloud infrastructure is changing what city data can do. The episode ends with a practical kind of optimism, tied to measurement instead of slogans. It is about giving cities better evidence, then letting them act on it. The result is a conversation that is patient, technical, and focused on real-world consequences. It works best for listeners who want to hear how AI translates into streets, lanes, corners, and policy choices rather than abstract claims.

As heard by us

A grounded look at how city data can shape safer streets.

This episode looks at how city data, micromobility cameras, and computer vision can make streets safer and easier to use. It stays most convincing when it moves from broad talk about AI to the small choices cities actually make, from a painted line to a full road redesign.

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Why you'd press play

You want AI explained through the streets you actually use.

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