How Edge AI Is Transforming Driver Safety

0:00 Introduction to Driving Innovation Podcast

Welcome to Driving Innovation, a podcast from Sonatus that explores technologies and solutions that are defining the automotive and mobility industries.

Today, we’re exploring how AI at the Edge is transforming driver risk assessment, insurance innovation, and in vehicle intelligence.

0:19 Guest Introduction: Siva Yoganathan

Joining me is Siva Yoganathan, Vice President of Engineering at MOTER Technologies.

MOTER is helping redefine Insurtech by moving beyond traditional underwriting models and leveraging real world driving behavior, contextual vehicle data, and embedded AI to create safer, more personalized mobility experiences.

0:39 Collaboration Between MOTER and Sonatus

In this conversation, we’ll discuss how MOTER and Sonatus are working together to deploy advanced AI models directly inside the vehicle using Sonatus AI Director, enabling real time driver coaching, scalable deployment, stronger privacy protections, and a fundamentally new approach to behavior based insurance and safety.

1:09 Understanding MOTER’s Role in Risk Intelligence

Sival, welcome to Driving Innovation. Let’s start with a quick introduction to and your role there.

Thank you, Sanjay, for having me on this podcast. So MOTER is an insurtech company and we are focused on developing AI foundational models that are used for risk intelligence analysis on the edge. Our focus is not just about measuring risk, but actually about reducing risk. And also about not really using the vehicles for data collection, but actually building AI models that would help with interpreting them, analysing them, and bringing value right on the edge.

And that’s why MOTER focuses and as the name suggests, MOTER is “Mobility on the Edge in Real Time”.

Okay.

So that’s our name. That’s clever.

Thank you. So as I understand it, MOTER’s approach goes well beyond traditional usage based insurance models. Can you explain what’s fundamentally different about your driver risk analysis model and why contextual driving intelligence, you put a lot of emphasis on that matters so much in this context.

2:21 Traditional vs. Contextual Risk Analysis

Right. So when you think about a traditional UBI, they’re mostly using brake acceleration turns based events, right, and speeding.

It’s true they bring value. But it’s important to understand the context in which particular driver may be applying a brake. Let’s take an example of a hard braking, right? So a person may be applying a hard brake because an object came in front of the vehicle in the same lane.

So that is avoidance. That’s wonderful. But what if the braking is due to a prolonged aggressive tailgating or a lane distraction or excessive lane departure? So those are risky events that can be corrected and can lead to less collisions, right?

3:08 Importance of Contextualization in Risk Assessment

Therefore, contextualisation is important. So in our foundational model building, we look at events by looking at the road scene inside the cabin and the basic telematic information to bring a collection of AI foundational models that are approved by Department of Insurance for assessing risk.

3:28 Edge Execution in Vehicle AI Models

Great, so you want to be able to eliminate false positives. If somebody is braking for legitimate reasons, you don’t want to penalize them for that. That totally makes sense. One of the most compelling aspects of this collaboration is that these models run directly inside the vehicle. Why was edge execution so important for Moter?

Right. Sanjay, I’ve been in the mobility space for a long time and I’ve worked on IoT and V2X and now I’m at insuretech with MOTER, where again, it’s so important that we are able to provide feedback in near real time. Take an example today.

On a typical day in the United States, tens of millions of vehicles are on the road. Imagine if each vehicle has to send all the vehicle data to the cloud to be processed, right? And then some of that key information, the consumer is in the vehicle. You’ve got to send it back to the driver to be consumed.

So it’s not effective, right? So when we are able to apply these AI foundational models on the edge, these phenomenal use cases can be consumed and used immediately. So that’s why an opportunity for us to be able to run our models on the edge is very important, right? It really streamlines the process.

Large data transmission is cut off, your user experience in the vehicle, and privacy is a key thing, right? Why do we have to send all the data to the cloud when it can be processed and analyzed right on the edge?

5:05 Role of Sonatus AI Director (now Fastlane Edge) in Deployment

Let’s talk about Sonatus AI Director specifically. What role did it play in making this deployment production ready?

So from an insurtech company like ours perspective, right, our core competency is driver behavioral analytics. So we spend a considerable amount of time building these complex AI foundation models. But that’s just one part of the problem. How do we deploy that?

You think about it, there are several dozen OEMs. Each has many makes and models. Yearly they distribute. And underneath that, there is several hardware infrastructure, OS infrastructures.

How do we then deploy these models? Right? So your AI Director is an amazing example, a great solution, where it actually abstracts all that complexity away from us, right? We focus on our core competency of driver behavioral risk analytics.

We build those models. We apply them in your infrastructure. Once we have an OEM relationship, yes, that’s necessary.

But the implementation infrastructure, you take care of it. Right? You abstract us from all those complexities. And therefore, it makes it so easy for us to be able to deploy across various OEMs, different brands, and still provide the same experience to the users.

6:27 The Many Uses of Risk Intelligence

Yeah, it makes a lot of sense. Now, your platform produces both insurance scoring outputs and driver coaching insights. How do you see OEMs and insurers benefiting from those dual capabilities?

You know, one of the challenges today in insurance, in the safety area, is the fragmentation, right? So insurance likes to measure risk of the particular driver and also the entire portfolio, right? And then the driver is focused about what is it they can improve on so the safety improves and also potential reduction in premium. So you have to have a single collection of foundation AI models that every entity, every stakeholder in that ecosystem, mobility ecosystem is using, right?

OEMs, right? So they also have a need, right? So they’re spending billions of dollars in ADAS and level one through five autonomous capabilities. But those vehicle capabilities, let’s take an example of the ADAS capability of tailgating, right?

It says you can set up the configuration to be one vehicle distance, two vehicle, three vehicle. But as a consumer, how do they know which setting is safer for them?

Right? There is a recommendation, the OEMs, but it’s not personalized to my safety characteristics. So these AI foundational models running right on the edge, right, stores that safety characteristics profile of the driver. It can recommend what setting is suitable for them.

Yeah. Right?

So I think the value of coaching is not just about giving immediate alert and event notification, but it’s also about helping the vehicle to be configured in a way that is safe for them, for their personalized experience. So we see that these AI models not only helping the insurance carriers assess risk, the drivers improving behaviors, how about infrastructure entities like DOTs and municipalities wanting to understand what type of behaviour is happening in their counties, sub counties, road network, and how to improve it. So all of them have to be able to run on the edge, collect, analyse the data. These AI models run on the edge, collect, analyse, process, and those process data goes and get distributed to the different users based on the UX experience they will like.

8:57 Future of Embedded AI in Vehicles

As you look forward to the future, where do you see embedded AI and in vehicle intelligence evolving next? Just generally speaking, obviously you’re involved in risk intelligence and so forth, but maybe even beyond that.

My honest belief is that the embedded AI is still in its early stages, right? And part of the problem is the ability to manage infrastructure deployment, right? We can build these amazing models, but we need a way to deploy and get the value out to the consumers, the customers, right? I think right now significant effort is being put on safety, let’s say from insurance perspective, this risk assessment, risk analysis models, immediate driver coaching, right?

9:42 Innovations in Preventive Risk Routing

I think the next step is preventive risk routing capability. For example, today, I want to go from point A to point B and I enter the destination address, the system then looks at congestion, you know, estimated time of arrival and a few other parameters to recommend a route. But imagine these AI based analytical models providing node level, like, you know, every intersection point or every mile, half a mile, whatever, some certain fixed points, it gives a scoring, right, risk score. And if they get sent to the cloud, now your routing now can be determined based on where you’re heading, right?

And then looking at, this route has ten stop signs and many of them had a high risk profile, then let’s route it in another route, right? So that’s the next level of routing innovation I believe the edge AI will help. And third one we already touched upon is the personalized experience in the vehicle. Today you can get in a vehicle and maybe some of them have like option one, two, three, you select a preset memory and that’ll adjust your seat, your steering wheel and maybe even remember your music and so on, right?

11:04 Conclusion and Future Outlook

This is fascinating. Well, Siva, thank you for joining us and sharing how MOTER is helping reshape the future of Insurtech and intelligent mobility. This collaboration truly highlights the growing importance of edge AI in the vehicle, not only for enabling smarter insurance and driver coaching, but also for improving privacy, scalability, and real time responsiveness across the connected vehicle ecosystem.

11:26 Closing Remarks and Next Steps

Thanks for listening and stay tuned for more conversations with innovators shaping the future of software defined vehicles and automotive AI.