AI in Automotive Podcast (Jayesh Jagasia)

In this episode of the AI in Automotive Podcast, we are following the incredible journey of Ocado from a retailer to a global technology business that offers a cutting-edge technology platform to other retailers. We talk to David Sharp, Head of Head of Autonomous Mobility at Ocado Technology, and he talks to us about their journey - bold and audacious in more ways than one.

Three things really stood out for me from this very enlightening conversation with David.

One, the way Ocado experiments with, integrates into and scales AI technology for various use cases in their business.

Two, the way Ocado is using the composite of traditional statistics, new generation AI and time-tested human intelligence to deliver dramatic order-of-magnitude impact in their business. 

Three, the autonomy experiments Ocado is running in moving goods from their warehouses to their customers, and what lessons might that have for the larger automotive and mobility industries.

Ocado’s journey to this point, and their ambition for the future offers so much inspiration for the larger automotive industry. Will we ever see an automotive retailer create a technology stack that they sell to OEMs and other retailers? Never say never!

AI in Automotive Podcast

Automotive product development is not just incredibly expensive, but also an extraordinarily complex process. Everything you see on a production vehicle is, at the end of the day, the result of hundreds of tug-of-war contests. Design and cost, manufacturability and serviceability, power and weight. The list goes on. Dozens of teams with hundreds of engineers pursuing conflicting design objectives, but eventually finding an optimum through a series of very difficult trade-offs.

Any benefit an automaker can take in the design and development process that accelerates take-to-market and improves margins is an absolute no-brainer. So what role can machine learning play?

In this episode of the AI in Automotive Podcast, I am joined by Dr Richard Ahlfeld, CEO of Monolith. We discuss how machine learning can be a potential game-changer in automotive R&D, and deliver some serious acceleration and cost benefits in the design and development process. 

Complex mathematical equations, ketchup bottles and a million dollar prize. This episode of the show has it all!

AI in Automotive Podcast

90% of all the data in the world was generated in the last 2 years. I have read this statistic in a news report from 2012, and a news report from 2019 and a news report from 2021. If this statement is true every year, you can begin to imagine the rate at which data generation and use is expanding. Actually that number is so big, you probably can’t imagine it. 

As someone famous once remarked: with great data comes great responsibility. Infinite amounts of data raise questions about the ownership of data, the uses of data and the ethics surrounding data. 

These are all themes we explore with Richard Barlow, Founder & CEO of wejo. Wejo is on a mission to be the data ecosystem and the comms stack for the automotive industry. Richard brings some really powerful counter-intuitive perspectives to our discussion, that will challenge all the cliches you have heard about data, data usage, data ownership and data security.

If you liked my chat with Richard on this episode of the AI in Automotive Podcast, please do share this episode with your colleagues and connections, and be sure to rate us on Apple Podcasts.

AI in Automotive Podcast

The digital screens in the car are the latest battleground in the battle for the driver’s attention. The main competitor? The mobile phone. 

Each time you choose to connect your smartphone to your car’s infotainment system using CarPlay or Android Auto, the mobile phone wins. The car’s digital surfaces are reduced to dumb screens that mirror the phone’s apps and use the phone’s processing power. Your usage data and analytics are no longer accessible to the car maker, putting them at a significant disadvantage.

So, can car makers, famously still on the software learning curve, compete with the Apples and Googles of the world - companies that build software for a living - in this software-led automotive world?

In this episode of the AI in Automotive Podcast, we are hosting Alex Barth, VP of Automotive and Mobility at Mapbox. Mapbox is a mapping and location cloud platform that offers the building blocks; the SDKs and APIs to power navigation. Their new product, Mapbox Dash, helps car manufacturers create brand-specific in-car digital experiences anchored to navigation, and generate subscription revenue. 

Alex shares some amazing insights on how automotive manufacturers can use the power of AI to deliver compelling in-vehicle digital experiences. 

AI in Automotive Podcast

A number of companies are trying to crack the autonomous driving puzzle, and a variety of approaches have evolved. Some companies are taking a software-first approach, building an AD software stack that can work on any hardware environment. Others are taking a hardware-first approach, creating a sensor and hardware environment that can adopt any software stack. Others still are creating a ‘walled-garden’ with software and hardware designed in close conjunction, allowing each to work only with the other for a specific use case. 

Imperium Drive, a UK-based autonomous driving startup, has a radically different take on the autonomous driving problem. Imperium Drive believes that a ‘human-in-the-loop’ is a critical stop on the journey to full autonomous driving. The company is focused on bringing commercially and operationally viable products to the market, even as it pursues its ultimate goal of making autonomy a reality.

We caught up with Koosha Kaveh, CEO of Imperium Drive on this episode of the AI in Automotive Podcast. Koosha shares his view of the evolution of AD, and progress on the autonomy journey. He also introduces us to Fetchcar, their super interesting driverless, human-in-the-loop car rental service.

I hope you enjoy listening to this episode, and if you do, please do share it with your network on LinkedIn and rate our show wherever you get your podcasts.

https://www.ai-in-automotive.com/aiia/120/kooshakaveh

AI in Automotive Podcast

It has been just over three years since I started the AI in Automotive Podcast. With all of the interesting ways in which artificial intelligence and machine learning are shaping the future of the automotive and mobility industries, I am happy to have hosted quality conversations exploring this theme further. 

My intention with the AI in Automotive Podcast was - and continues to be - to invite interesting people doing interesting things with AI in the automotive industry, and engage them in thought-provoking and enlightening dialogue.

The twenty episodes we have published since have had over five thousand downloads, and I am incredibly grateful to the regulars amongst you who have kept us going.

A lot has changed since 2019, and I imagine the pace of change will only accelerate in the coming years. With this, I am aiming to publish more regularly. We will soon release the long-overdue Season 02 of the AI in Automotive Podcast. 6 episodes, published once every alternate week. The same conversations, only better.

I might be slightly biased here, but I think if you are associated with the automotive and mobility industries in any capacity, then this podcast is essential listening for you to stay connected with the fascinating changes we are seeing around us. 

So, do me a favour - go ahead and share a link to the AI in Automotive Podcast with your friends and colleagues. You can find us wherever you get your podcasts.

Continue to tune in, and hear from some of the smartest people in our industry.

AI in Automotive Podcast

Over ten years ago, an idea that captured our imagination was that software is eating the world. Well, software may not have eaten the automobile, but it is certainly transforming it in very profound ways. 

The modern car is incredibly complex - not just from a hardware perspective, but also from a software point of view. Today’s software-defined vehicle is made up of numerous subsystems, each run by its own code; hundreds of sensors generating tonnes of data every second; and a number of vehicle functions now automated to a large degree. So how does this all work together?

We invited Sarah Tatsis, SVP of IVY Platform Development at BlackBerry to help us understand exactly how. In this episode of the AI in Automotive Podcast, Sarah unpacks the automotive software stack for us - from the foundational operating system software that helps various subsystems communicate with each other, to middleware that uses machine learning models to turn sensor data into actions, to application software on top that will help your car do amazing things like pay for your coffee in the future. 

I thought my cutting-edge new car was smart, but what stuck out most for me through this conversation is the huge amount of headroom available yet for our cars to get smarter and more connected to the world around us. 

I hope you enjoy my conversation with Sarah. If you do, please do share the AI in Automotive Podcast with your colleagues who might enjoy it as well.

https://www.ai-in-automotive.com/aiia/201/sarahtatsis

AI in Automotive Podcast

What would you define as automotive? Sure there are the cars, motorbikes, vans, trucks and so on. But what about lawn mowers?

We are stretching the definition of automotive in this edition of the AI in Automotive Podcast. And you will see why. Today we are speaking to Andres Milioto, a Senior Vision Engineer at Scythe Robotics. This company has developed an all-electric, fully-autonomous commercial mower, which, needless to say, uses machine learning extensively, primarily for perception.

The way the Scythe team has identified this very unique - but very large problem - and solved it using machine learning - I find that really cool. They are well on their way to solving two of the biggest problems this very traditional industry is facing - a perennial labour shortage, and pollution.

I was keen to bring Andres on the show to gain a deeper understanding of the similarities, and most importantly, the differences between Scythe Robotics’ application of autonomous technologies and what we might consider more conventional autonomous driving applications.

I hope you enjoy this very unique episode of the AI in Automotive Podcast. 

AI in Automotive Podcast

Connected vehicles to me, for the longest time meant a car that has a SIM card and is connected to the Internet as an IoT device. But connected vehicles are, and should be, so much more than that. In a world where vehicles are able to communicate with each other, with other participants on the road and with infrastructure, the possibilities that can unlock are endless. 

So what is coming in the way of that happening? Technology? Policy? Universal standards? What role can startups, private corporations and government bodies play in accelerating the evolution to a world of truly connected, V2X equipped vehicles.

We invited Wejo’s Sarah Larner to get her perspective on V2V, V2X and all things connected car. Sarah helps us make sense of these topics, and shares with us how Wejo’s vast dataset of 20 trillion data points forms the foundation of the automobile’s future. We discuss how the impending explosion of V2X data will help automotive AI applications go from being reactive to proactive to predictive. 

If you like my conversation with Sarah, do head over to the AI in Automotive Podcast on Spotify and Apple Podcasts and give us a thumbs up. Do share our show with your friends and colleagues who are excited by all things automotive. 

AI in Automotive Podcast

Electric Vehicles might look and drive like normal cars, but scratch beneath the surface and you will realise that they are fundamentally different at an architectural level. 

With the modern car being so much more than merely its mechanicals, I learnt that digital architecture in cars is a thing. The hardware - system on chip, or SoCs, processors and screens, combined with the software - the operating system, middleware and applications bring to life so many elements of the modern car that we take for granted.

I wanted to learn more about how these elements of the vehicle’s digital architecture work together, and so I invited Anshuman Saxena on the AI in Automotive Podcast. Anshuman is the Head of ADAS/Autonomous Driving at Qualcomm Technologies. He opens up the software-defined vehicle’s digital architecture for us, and introduces us to the Snapdragon Ride Platform. We talk about the platform’s potential to accelerate the development and deployment of autonomous driving technologies, and the direction this space is headed in, in the near future.

If you like my conversation with Anshuman, do head over to the AI in Automotive Podcast on Spotify and Apple Podcasts and give us a thumbs up. Do share our show with your friends and colleagues who are excited by all things automotive. 

AI in Automotive Podcast

Buttons and physical interfaces disappearing from your car is now an inevitability. That said, we certainly can’t be fumbling with a touchscreen to change the fan speed or switch the radio station. There has to be a better way. That’s what makes me very bullish about voice as the primary human-machine interface in the modern car.

We have all gotten used to speaking to our smartphones and smart speakers, and getting a lot done - typing out an email, playing your favourite 60s rock album and ordering toilet paper. The voice experience in the car, however, lags far, far behind.

SoundHound is here to change that. In this episode of the AI in Automotive Podcast, I am speaking to Matt Anderson, SoundHound’s Director of Business Development. Matt lays out exactly how SoundHound’s speech-to-meaning technology is able to understand what you are saying, interpret your intent and respond to you intelligently, whilst tapping into a variety of domains. In addition to the technology itself, we also talk about a brand’s voice identity, which I found incredibly fascinating. 

I am excited about what the future holds in this space, and after listening to my conversation with Matt, I am sure you will be too. And when that happens, do share this episode of the AI in Automotive Podcast with a friend or colleague.

AI in Automotive Podcast

Fancy waking up one fine day to find that your car, much like your smartphone, now has a better interface on the infotainment touchscreen, or that annoying niggle that was draining your battery has magically been resolved?

The essence of software-defined vehicles is their ability to keep getting better over time. A lot needs to happen behind the scenes, for this to work. How is data from a fleet of vehicles moved into the cloud? How do engineers use this data to identify patterns and improvements? And how are improvements to the software pushed back to the fleet?

To learn more about some of these themes, I invited Hemant Sikaria to the AI in Automotive Podcast. Hemant is the CEO and Co-founder of Sibros, a software company headquartered in Silicon Valley. Sibros helps automotive OEMs and mobility companies power the connected vehicle ecosystem with their Deep Connected Platform.

This is an incredible discussion that will help you learn more about the foundation that enables using software and AI to make our vehicles better over time. If you like my conversation with Hemant, do share the AI in Automotive Podcast with a friend or colleague, and drop us a rating wherever you get your podcasts.

#ai #automotive #mobility #technology #podcast #softwaredefinedvehicle

https://www.ai-in-automotive.com/aiia/206/hemantsikaria

AI in Automotive Podcast

There has been a lot of talk recently about vision versus LiDARs and RADARs. I hosted Leaf Jiang, CEO of a company called NODAR to learn more about the advantages and limitations of each technology, and how NODAR's own technology overcomes them. Their name is a nice play on the fact that their product is not RADAR or LiDAR, but in fact, uses vision to achieve resolution and depth perception better than either of them.

Instead of relying on machine learning models to interpret the feed from the cameras, NODAR’s system, consisting of a pair of cameras, triangulates distance measures to points in the scene by measuring angles to the point from each of the cameras. There’s a lot of complicated geometry involved, which, sadly for the nerds amongst you, we will not go into.

All that said, NODAR’s colour-coded point clouds can be an incredibly powerful source of data for machine learning models that can then do everything from scene inference to path planning, possibly computationally more efficiently. 

I am sure you will love listening to my chat with Leaf on this episode of the AI in Automotive Podcast.

AI in Automotive Podcast

If you, like me, grew up using Windows 3.1, then you are familiar with the dreaded Blue Screen of Death. One of the reasons for that blue screen - in simple terms - was that the computer had run out of resources to run all the tasks that were being demanded of it. 

I have news for you - that reality may be coming to your car sooner than you think. We are putting increasing amounts of computational demands on the modern vehicle. The increasing number of sensors, the increasing resolution of many of these sensors, and computationally intense AD/ADAS tasks mean that current EE architectures and chips are running out of steam. It is believed that the modern car requires ten times the processing capability offered by current architectures. 

So, how does the industry stop cars from freezing up under the burden of heavy computation tasks?

Enter Marc Bolitho, one of the most knowledgeable people in the automotive semiconductor space. Marc is the CEO of Recogni, a company that has created an incredibly disruptive processor that seems to have achieved the performance-power consumption holy grail. I had a remarkably enlightening conversation with Marc, and we spoke about the fundamentals and design process of EE architectures, the limitations of current processors, and how Recogni’s product promises to meet the computational demands of a modern car without breaking a sweat.

If TOPS means nothing to you, and EE is a telecom operator in the UK, then you have to listen to my chat with Marc. Season 3 is full of fantastic conversations like this one, so stay tuned, and do spread the word about the AI in Automotive Podcast, a platform for dialogues on how AI is shaping the future of the automotive and mobility industries.

AI in Automotive Podcast

LiDARs are an important piece of the autonomous driving and ADAS puzzle. While they boast impressive resolution and frame rates, they have also built a reputation for being big, bulky and expensive. Can there be another way?

Paul Drysch, CEO of PreAct Technologies certainly thinks so. PreAct has been working behind the scenes for a number of years to develop their short-range LiDAR which aims to deliver all the functionality of a LiDAR at short distances while addressing the biggest drawback of the technology - its cost. Their software-definable LiDAR is to the world of LiDARs what the software-defined vehicle is to traditional cars.

Join Paul and me on this episode of the AI in Automotive Podcast as Paul gives us a crash course on LiDARs, their types and flavours. We also talk about what the sensor suite in future cars might look like, and where PreAct’s low-cost, short-range LiDAR fits in. Paul believes LiDARs’ time in automotive is yet to come. I am so excited about how technologies like PreAct’s can expand LiDARS’ use cases, and accelerate their mainstream adoption.

https://www.ai-in-automotive.com/aiia/302/pauldrysch

AI in Automotive Podcast

In the world of autonomous driving, high-compute GPUs are all the rage. So I was incredibly delighted to learn of a company that is taking a very counter-intuitive approach to the perception stack. These guys have identified a number of use cases that do not require the 100% accuracy that autonomous driving demands, and are focused on making their vision perception stack work on smartphones you can buy for a hundred dollars.

In this episode of the AI in Automotive Podcast, I am pleased to host Jorit Schmelzle, co-founder and Chief Product Officer of Peregrine Technologies. This is such a wide-ranging conversation, covering topics from sensor fusion, to understanding the automotive perception stack, and how ordinary smartphones can deliver interesting use-cases for the vision and perception stack.

While the world of autonomy started with the desire to make a fully-autonomous vehicle that would then power a fleet of robotaxis, the way the space has evolved has created some very compelling use cases, even if full autonomy is still a few years away. Peregrine Technologies is at the forefront of delivering real value today, while also inventing the technology of tomorrow.

I am sure you will enjoy my chat with Jorit, so can I ask you to please share the AI in Automotive Podcast with your friends and colleagues who carry an interest in AI, or the automotive industry.

#ai #automotive #mobility #technology #podcast #perception #selfdriving #autonomousdriving

AI in Automotive Podcast

Have you ever thought about how autonomy came to be? What were the origins of the idea of autonomous vehicles? Have you ever wondered what is next for autonomy, and how will this space evolve in the future?

Well, wonder no more. On this episode of the AI in Automotive Podcast, I am delighted to host an OG member of the autonomy gang, whose connection with autonomous driving goes way back to the heady days of the DARPA Grand challenge almost two decades ago.

Bibhrajit Halder, Founder & CEO of SafeAI joins me to share how the idea of autonomy originated, how it evolved from 1.0 to 2.0, and how it might evolve in the future to 3.0 and 4.0. We also talk about the application of autonomous driving in the mining and construction industries, and how SafeAI is using AI to seriously disrupt the mining industry.

I never thought I would be discussing mine economics on this show, but we did, and it was so much fun! I hope you enjoy my chat with Bibhrajit, and if you do, why don’t you go ahead and share the AI in Automotive Podcast with a friend or colleague who carries an interest in this space.

AI in Automotive Podcast

Designing a car is hard. 🚙 🏎️ 🏁

It involves solving a series of multi-dimensional problems under a variety of constraints. I am oversimplifying here, but a problem with 8 dimensions and 5 values for each dimension will have 390,625 combinations to be experimented with. Real-world problems are usually way more complex, and testing every unique combination of inputs in an experiment is often not viable. So what is the solution, and what role does AI play?

Gary Brotman, CEO of Secondmind, joined me on the AI in Automotive Podcast to share the answer to that question, and talk to us about the role of AI in accelerating the automotive design process. Think of Secondmind’s platform as a co-pilot for automotive engineers. It uses the power of AI to help them design the right experiments to solve complex multi-dimensional problems of the sort they encounter in their day jobs. This focuses their limited resources on a subset of the problem space, and gets them to the optimal solution in the most time-efficient and resource-efficient manner, accelerating the design process.

This is a fascinating conversation with one of the most experienced and knowledgeable blokes out there when it comes to AI in Automotive. I hope you enjoy my chat with Gary as much as I did. And if you do, go ahead and rate the AI in Automotive Podcast wherever you listen to podcasts.

#ai #automotive #mobility #technology #podcast #engineering #designofexperiments #machinelearning

https://www.ai-in-automotive.com/aiia/305/garybrotman

AI in Automotive Podcast

Connected cars have been around for a while, but in-car services have strangely not really taken off. I learnt in my chat with Todd Thomas, Chief Revenue Officer at AiDEN Auto that there’s a good reason. Or four. Todd spoke to me about the evolution of the modern car to become a more mature connected device from its current state, and in the process we unearthed some real gems of insight. 

After today’s chat with Todd, I have a much better understanding of what connected vehicles 2.0 might look like, and why we may just be at the cusp of an explosion in true in-car services, many of them powered by AI. I am also convinced that the way we interact with our cars is going to undergo a significant shift, substantially changing our relationship with our cars. It’s going to be an exciting next few years in this space. Subscribe to the AI in Automotive Podcast to stay in touch with the technologies shaping our industry’s future.

AI in Automotive Podcast

Radars have been evolving at a really rapid clip, helped in no small part by innovative companies like Arbe Robotics. On today’s episode of the AI in Automotive Podcast, I am talking to Ben Rathaus, VP of AI and Perception at Arbe.

Ben talks us through the history of radars, and how and why they found their way onto cars. We discuss how Arbe’s silicon and software is creating an order of magnitude improvement in the resolution and performance of automotive grade radars. We talk about the composition of radars, and their output - a mapping of the free space around the vehicle - an absolutely key building block of AD and ADAS algorithms.

Ben and I started at cosmology and ended up at what the humble radar might look like in the future! Just another fascinating conversation that allowed me to understand the past and future of radars a lot better, as well as the very important role they play in making our cars smarter and safer. I think of them as the invisible, unsung heroes - working away diligently in the background, making everything around them work a lot better.

If you have ever wondered whether future radars can wholly replace cameras on the car… well, you will find out at the end of my chat with Ben. So go have a listen, and if you like what you hear, do share the AI in Automotive Podcast with a friend or colleague.

#ai #automotive #mobility #technology #podcast #radar #sensors #sensorfusion

AI in Automotive Podcast

Autonomous Driving is a big enough paradigm shift. But after years of research and billions of dollars spent trying to get cars to drive themselves, perhaps it is time for a paradigm shift within a paradigm shift. What might this look like?

Daniel Langkilde, CEO of Kognic joins me on the AI in Automotive Podcast to discuss exactly this. Daniel and I talk about the current approach to autonomy, which involves breaking down a very complex problem into its components - perception, prediction and planning - and its limitations. Based on a better understanding of how humans actually go about accomplishing the task of driving, we ask if perhaps it is time to take a different approach to delivering autonomy at scale. We discuss a key component of this approach - the world model - or the ‘common sense’ that a machine must be equipped with to make sense of the complex world around it. Daniel also talks about alignment, what it means to steer a system towards accomplishing its stated goal, and its relevance to autonomous driving.

I am convinced that we are far from done with solving autonomy. On the contrary, I feel there is a lot of unexplored territory yet, which can dramatically change how we approach this opportunity. I hope my chat with Daniel gave you a sneak peek into what the inception of paradigm shifts looks like, and what it means for the future of autonomous driving. If you enjoyed listening to this episode of the AI in Automotive Podcast, do share it with a friend or colleague, and rate it wherever you get your podcasts.

AI in Automotive Podcast

Vehicle quality issues that lead to recalls and lawsuits cost automotive OEMs tens of billions of dollars in cost and lost revenue each year. Given the explosion of connected vehicle data, one might expect that this data could be leveraged to reduce this cost. Things are rarely that straightforward. Why is that?

I invited David Hallac, CEO of Viaduct to the AI in Automotive Podcast to find out more. David’s 5-year old startup finds patterns and relationships amongst billions of connected vehicle data points, and delivers two powerful, commercially sound use cases to automotive OEMs. One, it helps automotive OEMs proactively identify and address quality issues, saving hundreds of millions of dollars in warranty costs and recalls. Two, it helps predict failures, call vehicles in for proactive maintenance, and helps bump up up-time - a god-send, especially for fleet customers. 

The big penny drop moment for me during my conversation with David was that connected vehicle applications don’t have to be bold, visible and sexy, delivering massive incremental revenue at near 100% margin. In fact, the connected vehicle applications most likely to succeed in the near-term are those that deliver commercial value today, often by way of substantially reduced costs. Viaduct’s quality management and maintenance prediction use cases check those boxes, and how. Listen to my chat with David to find out more.

If you enjoyed my chit-chat with David Hallac, please give the AI in Automotive Podcast a solid five stars on Apple Podcasts and Spotify - I am always thankful for your support.

#ai #automotive #mobility #technology #podcast #machinelearning #unsupervisedlearning #warranty #recalls #maintenance #quality

AI in Automotive Podcast

Since the beginning of time, cities have been incredibly important to civilization. Today, the World Bank estimates that cities contribute 80% of global GDP. Cities are central to our growth and prosperity, but every single major city in the world is facing challenges ranging from poor air quality to creaking infrastructure. 

So how do cities evolve to prepare for the future? And what role does AI play in this evolution?

On this unique episode of the AI in Automotive Podcast, I invited the CEOs of two companies that are enabling our cities to become safer, smarter and more sustainable using the power of artificial intelligence. 

Andrew Fleury is the CEO of Luna Systems, a company that is making mobility smarter using their computer vision capabilities. They are putting cameras on micromobility scooters, and using AI to help micromobility operators give their riders a safer experience.

Chris Tingley runs EVWare, a company whose hardware and software platform makes vehicles safe, connected and intelligent. They do this by bringing high-tech features and functionality to vehicles of all shapes and sizes, including micromobility scooters.

The modern city generates bucketloads of data. It has been for a while now. Till a few years back, there was limited use for this data. Perhaps the quality of data was suboptimal. Perhaps it was in a form that was not adequately usable to identify patterns and generate insights. Maybe we did not have enough tools and infrastructure to leverage this data.

All that is changing fast. With the rise of AI and the commoditisation of cloud infrastructure, the data that cities generate carries immense potential in improving decision making and crashing decision time by orders of magnitude. Companies like Luna Systems and EVWare are - in their own way - creating a collaborative ecosystem of partners that can make our cities smarter, safer and more sustainable.

I hope you enjoy listening to my chat with Andrew and Chris. If you do, go ahead and rate the AI in Automotive Podcast wherever you get your podcasts. 

#ai #automotive #mobility #technology #podcast #machinelearning #urbandesign #cities #infrastructure #vision #micromobility

AI in Automotive Podcast

Till a few weeks back, Cruise was considered one of the big three of autonomous general driving. It was licensed to run a robotaxi service in San Francisco, and my LinkedIn feed was full of folks gushing over the magical experience of being driven around in a car without a driver.

Then the proverbial shit hit the fan. One of Cruise’s robotaxis got caught in a classic edge case, with a road user who was hit by another vehicle, falling in its path. So far so bad, but then things got worse.

In the last few weeks, heads have rolled. Cruise has seen the departure of its CEO and other key execs. The company, owned by GM, has decided to get rid of a quarter of its staff, and finds itself in a proper existential crisis.

How did things come to this, and could they have been avoided?

To find out, I invited Alex Roy to the AI in Automotive Podcast. Alex is one of the most recognised voices, and an absolute authority in this space. He wears many hats, amongst which is hosting the very popular Autonocast podcast. Previously, Alex worked as an exec at Argo, and was key to their thoughtful approach to operationalising self-driving cars on public roads.

While my conversation with Alex started talking about Cruise, the theme is not about Cruise alone. Because there is a long tail of edge cases, and things are going to go wrong as this very nascent technology is brought to market. This is also a very new space, and as one might expect, regulation needs to find the right balance between encouraging innovation and guaranteeing safety. The technical scale of the problem can not be underestimated, and it rarely is. But it is the human side of the problem that often does not get the attention it deserves. My chat with Alex underlined for me that getting the human and cultural piece right is going to be as critical to the success of autonomous driving as solving the technical problem.

With this, we season 4 of the AI in Automotive Podcast is a wrap. I am certain you enjoyed listening to my chat with Alex on season four’s final episode. Please do share the episode with your friends or colleagues, or drop a note on your socials - I always appreciate your support.

#ai #automotive #mobility #technology #podcast #selfdriving #autonomousdriving #safety #leadership #cruise

https://www.ai-in-automotive.com/aiia/406/alexroy

AI in Automotive Podcast

If you needed a surprise in the new year, this is it! We are back!! It’s been a longish hiatus. I needed the time to recharge and think about where the industry is headed. We also welcomed a new member to our family, and I have used the time to change over a thousand nappies!!

So, what exactly happened in the last year? 

The AI in Automotive Podcast quietly - literally quietly - turned five. 

AI went properly mainstream. Like BOOM mainstream! The silicon valley AI CEOs turned into demigods, and everything from your toaster to your kids’ toys turned AI-powered, seemingly overnight.

AI was always an important enabling technology in the automotive industry, and this is what we had been covering on the show for the last five years. But the buzz around AI pushed some OEMs to make AI in the car more visible - enter features like ‘ChatGPT in the car’. There are interesting applications for a conversational interface in the vehicle - we covered this in our chat with Matt Anderson from SoundHound, for instance. But for them to have any value to the driver, software-defined vehicles with a coherent EE architecture is a key pre-requisite. We’ve covered this in numerous conversations in the past. With Sarah Tatsis from BlackBerry for instance, or Hemant Sikaria from Sibros.

If you look beyond what is mainstream, then the biggest theme that is emerging is the rapid convergence of the automotive and energy industries. We are seeing energy utilities turn into EV lease companies, and automotive OEMs turning into energy companies, thanks to the energy in the batteries of their EV parc. This represents significant opportunities for energy companies, automotive OEMs, fleet owners / operators and hundreds of other players in the automotive and energy ecosystems. 

Needless to say, AI, as an enabling technology, is a key accelerator of this convergence. This is what we will be covering in future episodes of the AI in Automotive Podcast. 

As always, I have an incredible roster of knowledgeable experts sharing valuable insights and unique perspectives. I am sure you will enjoy what is coming up.

If you feel you have benefited from the content on this show in the past, please do share the AI in Automotive Podcast with your friends and colleagues who might find it valuable as well. I promise you there are no more babies on the way, and hence, no more long breaks!!

AI in Automotive Podcast

Range anxiety. Charger anxiety. This anxiety and that anxiety. There seems to be a lot of anxiety in the mainstream adoption of EVs.

One of the biggest behaviour changes someone migrating from years of driving an ICE to an EV has to make is the refuelling behaviour. You refuel an ICE when you have. You recharge an EV when you can. You charge every time you park. At home, at your workplace, at your gym, your shopping center, at the restaurant. For this to become reality, charging must become ubiquitous.

But it’s not that easy. It never is. So many chargers are likely to burden the grid, which, let’s face it, was never designed to deal with huge loads coming on simultaneously. So what’s the solution? And how does AI help?

I invited Teddy Flatau to kick off season 5 of the AI in Automotive Podcast. Teddy is the founder and CEO of Wevo Energy, a company that helps solve exactly this problem with the power of technology and AI.

Charging infrastructure sits squarely at the intersection of automotive and energy. It is often touted as the missing piece in the electrification of transport, and so, understandably, it attracts a lot of attention. However, hiding in plain sight is the opportunity that cheap, reliable AC chargers at multi-family homes, commercial complexes, retail parks, hotels presents - any destination with more than an hour of dwell time is ripe for a bank of cheap and cheerful AC chargers. Making these chargers smart in terms of when they charge which car based on what parameter while being friendly to the pocket and the grid is what can truly unlock the ubiquity of EV charging. This is the transformation that Wevo Energy is bringing about.

As one of the most knowledgeable voices in this space, Teddy shares his view on what it will take for charging to be everywhere, and how Wevo Energy is helping the world get there. If you are interested in the smarts that make a charger go from a dumb plug to a cutting-edge piece of tech that can manage when and how much your car charges to optimise your spend and the load on the grid, this is the chat you want to listen to.

I hope you enjoyed my conversation with Teddy, and if you did, why not share theAI in Automotive Podcast with a friend or colleague. We will be back soon with more perspectives on how AI is driving the convergence of automotive and energy.

#ai #automotive #mobility #technology #podcast #energy #evcharging #smartcharging

https://www.ai-in-automotive.com/aiia/501/teddyflatau

AI in Automotive Podcast

Your EV is so much more than a car to take you from point A to point B. The energy it stores in its battery is a whole world of possibilities when you are not using the car. Which, let’s face it, for most of us, is the vast majority of the day. During these times, your EV is a ‘decentralised energy resource’ - one that can help the grid in a variety of ways.

One of the most interesting ways in which EVs are accelerating the mobility-energy convergence is through virtual power plans, or VPPs. On the one hand, EV fleets aggregated as a VPP provide value to the grid by offering flexibility and balancing services. On the other, it allows fleet owners, operators and vehicle owners to benefit financially, reducing their charging bills or their lease payments. 

But how does this come to life, and what role does AI play in this orchestration?

In this episode of the AI in Automotive Podcast, I invited Johanna Izett from Hive Power, a Swiss software startup that is making our grids smarter by connecting energy assets like EVs to offer grid services and unlock value for all stakeholders. Johanna shared her insights about VPPs, what makes them tick and how traditional automotive OEMs must rethink the proposition that they offer to their customers.

Hive Power is one of those fascinating companies at the intersection of automotive, energy and tech that are accelerating the convergence of the mobility and energy industries. Hive Power’s Flexo platform orchestrates the delivery of flexibility and grid balancing services, making the grid smarter and more resilient, while enabling fleet owners and operators to generate additional revenue from their EV fleets. Hive Power’s software suite also allows OEMs to offer so much more than metal, which I believe is going to be an important differentiator in the EV age.

If you want a sneak peek into the possibilities EVs hold, then this is the interview you don’t want to miss. If you enjoyed my chat with Johanna, please do share the AI in Automotive Podcast with a friend or colleague.

#ai #automotive #mobility #technology #podcast #energy #evcharging #smartcharging #gridbalancing #flexibility

https://www.ai-in-automotive.com/aiia/502/johannaizett

AI in Automotive Podcast

If you are a fleet manager responsible for electrifying your fleet, prepare for your world to be rocked. The fleet manager’s decision-making approach and framework for a traditional ICE fleet is well-established. It has been in use for decades, and typically takes into consideration the acquisition cost and operating cost of vehicles to calculate total cost of ownership. It then compares that with the revenue generated by the vehicle over its lifetime, and helps fleet managers make informed decisions about their fleet.

With EV fleets, the whole process and its associated tools are going to be upended. Not only are acquisition and operating costs for EVs vastly different from ICE vehicles, fleet managers must take into consideration a variety of other factors into their decision-making. For instance, setting up charging infrastructure at your depots. You need to consider the cost of setting up the infrastructure, the constraints of your power connection and the time and cost of upgrading your grid connection, should that be required. 

And this is just the beginning. Where things get really exciting is on the revenue side. Unlike an ICE, an EV can generate value for your fleet even when it is not on the road, but plugged into a charger.

How, you ask? I invited Mike Kent from GridBeyond and Alex Iriondo from Monta to join me on the AI in Automotive Podcast, and share with us exactly how. GridBeyond is a smart energy company that offers AI-powered energy services, connecting and automating energy demand to balance the grid. Monta, on the other hand, develops and deploys software solutions for the EV charging industry. Together, GridBeyond and Monta are on a journey to help fleet owners and operators maximise the value they derive from their fleet assets, for instance, when they are hooked up to a charger.

Fleet electrification can appear intimidating, but after this chat with Mike and Alex, I couldn’t be more excited about the possibilities that electrified fleets hold. We are just scratching the surface at this point in time, using fleet assets to deliver flexibility and grid services. But as the ecosystem matures, the sky is the limit - fleet assets aggregated as a Virtual Power Plant, vehicle to grid and vehicle to building - all of this is coming in the near future, and promises to revolutionise how fleets are acquired, operated and managed. 

If you are a fleet owner or operator, or just curious about the opportunities that exist as fleets electrify, you want to spend 40 mins listening to this fantastic conversation with Mike and Alex. If you do enjoy it and know someone who works with fleets, go ahead and share this episode of the AI in Automotive Podcast with them.

#ai #automotive #mobility #technology #podcast #fleet #depotcharging #energy #evcharging #smartcharging #gridbalancing #flexibility

https://www.ai-in-automotive.com/aiia/503/mikealex 

AI in Automotive Podcast

All around the world, hundreds of companies, big and small, are using AI technologies to bring an unprecedented level of innovation and disruption to the automotive industry. 

I am Jayesh Jagasia, and I host the AI in Automotive podcast. This is a platform for conversations about the rapidly growing role of AI in the automotive and mobility industries. Join me as I host experts in the domain to learn more about how AI is shaping the future of the automotive and mobility industries. 

AI in Automotive Podcast

The power of Artificial Intelligence and Machine Learning presents a huge opportunity to make vehicle valuations more accurate and more dynamic. Cazana is a company that is doing just that. In part 1 of the interview with Tom Wood, CEO of Cazana, we talk about how Cazana uses AI to solve a problem that is as old as the industry itself, and how its solution benefits dealers, OEMs, insurers and lenders.

AI in Automotive Podcast

The power of Artificial Intelligence and Machine Learning presents a huge opportunity to make vehicle valuations more accurate and more dynamic. Cazana is a company that is doing just that.

In part 1 of the interview with Tom Wood, CEO of Cazana, we spoke about how Cazana uses AI to solve a problem that is as old as the industry itself, and how its solution benefits dealers, OEMs, insurers and lenders.

In part 2, we dig deeper into the value of data, the risk of biases, and finally, what the future holds for the industry, as well as for Cazana.

AI in Automotive Podcast

Data and technology are great levellers. The power of data and AI is redrawing the automotive industry landscape, dramatically altering relationships and equations between OEMs and their suppliers. AI is also creating a new generation of industry players - from startups to consumer electronics giants.

What does the tech-driven redrawing of the auto industry landscape mean for traditional players in the industry? How does the rise of AI and related technologies change the equation between carmakers and their suppliers? How will traditional players compete with the Googles and Amazons of the world in the race to attract high-quality tech talent? What does the future of car ownership look like in the age of data?

We explore some of these themes with Paul Eichenberg, auto industry veteran and Chief Strategist.

In Part 1, we talk about how data and technology are redrawing the traditional automotive landscape, and powering the rise of a new generation of industry participants.

AI in Automotive Podcast

In Part 1 of my conversation with Paul Eichenberg, Chief Strategist at Paul Eichenberg Strategic Consulting, we spoke about how data and technology are redrawing the traditional automotive landscape, and powering the rise of a new generation of industry participants. 

In Part 2, we talk about talent wars, car ownership in the age of data, and how an interesting application of AI is making our cities smarter and safer.

AI in Automotive Podcast

Autonomous driving is widely considered to be one of the toughest problems to solve, and whilst there have been some blips on the way, the technology is evolving rapidly. This is thanks mainly to the rapid hardware and software advances in the field of Artificial Intelligence.

Traditional automotive brands, large automotive suppliers, hundreds of autonomous driving startups powered by billions of dollars of venture capital; all of them are locked in a fascinating race to make safe and reliable autonomous driving a reality.

But how exactly does an AV come to life? What goes on behind the scenes to make a car intelligent enough to be able to drive itself?

In this episode of the AI in Automotive Podcast, Dr Florian Baumann takes us on a whirlwind tour of the AV development process, and a fascinating journey into how AI is powering the rapid evolution of autonomous driving technology.

AI in Automotive Podcast

Why are Autonomous Vehicles a big deal? How far are they from reality, and what is the most likely path they will take to market? What sort of social, economic and cultural impact might they bring with them? 

We explore these themes in our conversation with Dr Reinhard Stolle. Reinhard is Vice President of Engineering at Autonomous Intelligent Driving, or AID. Reinhard has a wealth of experience in the automotive technology and innovation areas, and for over 15 years, has played leadership roles at global automotive OEMs. In 2008, Reinhard founded the central department for Software Architecture and Development at BMW Group. As Managing Director of BMW Car IT from 2012 to 2016, Reinhard built up organisational expertise in the areas of infotainment, driver assistance and autonomous driving. In his most recent role at BMW Group, Reinhard was VP for Artificial Intelligence and Machine Learning, and focused on AI for autonomous driving. 

AID, is based in Munich, and is bringing together top talent in automotive, software, robotics and AI. Working with the agility and mindset of a start-up and the powerful support and backing of Audi and the Volkswagen Group, AID is creating the backbone of a universal self-driving system.

Our conversation today dipped into Reinhard’s wealth of wisdom, and it was super enlightening for me. I hope you enjoy it too.

AI in Automotive Podcast

Like me, you’ve probably found yourself sitting in traffic, surrounded by vehicles of every size and shape, wondering why there isn’t a better way yet. The good news is that there is.

In this episode of the AI in Automotive Podcast, we are speaking to Noam Maital, CEO of Waycare Technologies, a fabulous company that I came across recently, that uses AI to solve a very real problem - that of traffic management and crash fatalities. company that provides solutions for a host of traffic management agencies. Waycare has built a cloud-based platform that pulls data from a variety of sources - city infrastructure, connected vehicles, navigation apps, and so on. It then uses AI to process this data and provide real-time insights to help traffic management, crash detection and incident prediction. 

One of the things that really struck me in today’s conversation is how much data we actually have sitting around, and how poorly utilised it can sometimes turn out to be. Waycare has unearthed just one area where putting the data to better use using the power of AI is yielding tremendous results. Think of what else might be out there...

Enjoy the chat, and if you like it, remember to rate us on Apple Podcasts, and share the podcast with a friend or a colleague.

AI in Automotive Podcast

We suffer a million and a half fatalities on the roads every year. The number of people who are injured in road traffic incidents is orders of magnitude higher. These numbers are staggering. But also very, very avoidable. 

Today we are talking to Chris Van Dan Elzen. Chris is a Vice President at Veoneer, a global leader in automotive technology. Veoneer is putting its considerable resources behind a bold and noble cause - halving the number of traffic fatalities and injuries.

How, you say? Veoneer builds some of the world’s most advanced ADAS hardware, software and systems. Their vision is to democratise access to this technology and make our roads a lot safer in the future.

So, what role does AI have to play in automotive safety? How does this technology bring to life so many advanced safety features that we take for granted today? And what are some of the challenges to democratising AI-powered automotive safety?

These are some of the topics we explore with Chris in our chat today.

Enjoy the show, and if you like it, remember to share our podcast with a friend or colleague.

AI in Automotive Podcast

In this episode of the podcast, we are joined by Venkat Sreeram, who shares his story and journey with us.

Venkat is an all-out automotive guy and a serial entrepreneur. He shares his very inspiring entrepreneurial journey with us, and talks about what led to him starting his latest venture, ClearQuote.

ClearQuote is using AI and computer vision to instantly identify and assess the damage on any vehicle. The platform is designed to benefit dealers, mobility providers and insurance companies by helping them automate the process of damage assessment and dramatically improve throughput.

http://ai-in-automotive.com/aiia/109/venkatsreeram

AI in Automotive Podcast

Some sources estimate that over 500 billion dollars have been invested already in the quest for full autonomous driving, and an equal amount is likely to be invested over the next few years.

Everyone is in this race - from tech companies, VCs and PE funds, sovereign wealth funds, neo manufacturers and traditional automotive industry players - OEMs and large Tier 1 suppliers. If you are in the automotive A-list, you are working on an AV. 

Are we witnessing an AV Arms Race?

On this episode of the AI in Automotive podcast, I am hosting Arun Kumar from AlixPartners. AlixPartners is a renowned turnaround and innovation consulting firm, with deep expertise in automotive, and Arun is a Managing Director based in the Chicago office. 

Arun and I talk all things AV - the hype cycle of the last few years, the sobering realisation of the present, and the challenges and opportunities of the future. Arun shares his perspective on the AV Arms Race, and what it will take to come out on top.

AI in Automotive Podcast

Data is the lifeblood of Artificial Intelligence. Quite simply, the better and richer the quality of data, the more capable the algorithm. Now this applies to both, the training data available to train the algorithm, but importantly, also the input data that is available for the algorithm to do its job.

Take the case of autonomous vehicles or advanced driver assistance systems. These systems rely on the eyes - cameras, LIDARs and RADARs - to see the environment around the vehicle. The input from these eyes is then passed on to the brain - the algorithm - which makes sense of what the eyes see. 

Most state of the art ADAS and AV algorithms today are designed to perceive what these sensors see by drawing bounding boxes around road users. That’s how they perceive pedestrians, other road users, vehicles and obstacles. 

But human behaviour rarely fits in a box. And human behaviour has a huge impact on how good or not an AV algorithm is. A bounding box alone is not sufficient to really perceive pedestrian behaviour, for instance. Is that pedestrian about to cross the road? How much risk does this road user pose? Is that a vulnerable road user?

Enter Humanising Autonomy. A company on a mission to create a global standard for human interaction with automated systems. This is an incredibly interesting company, and I was delighted to have the opportunity to speak to their Co-founder and Chief Product Officer, Leslie Nooteboom.

Think of Humanising Autonomy as a module you could add to the AV brain, that then makes the brain capable of perceiving - and predicting - human behaviour on roads. I would imagine a solution like this could improve road safety by orders of magnitude.

These guys are up to some really fascinating stuff that sits at the intersection of behavioural psychology, vision perception and artificial intelligence. How does that impact the world of autonomous driving? Find out in my very interesting chat with Leslie.

http://ai-in-automotive.com/aiia/111/leslienooteboom

AI in Automotive Podcast

GPS, or the Global Positioning System, is now ubiquitous as a way for us to pinpoint our location anywhere in the world, plot that location on a reference, often a map, and know where we are. Did you know, however, that even some advanced GPS systems can only deliver an accuracy of about 25 cm. This level of precision, while sufficient for you and I, just does not cut it for a vehicle equipped with advanced driver assistance and autonomous driving features.

Precise location data is absolutely essential for ADAS and AD functionality. Cameras, radars, LIDARs and other sensors can help the car ‘see’ its environment, but for the vehicle to make sense of the input from these sensors, it needs a reliable, effective way of plotting this world on a reference map.

I learned from my guest on the latest episode of the AI in Automotive Podcast that GPS has a number of limitations when it comes to ADAS and AD applications. In this episode, I am joined by Joel Gibson, Executive Vice President of Automotive at Swift Navigation. Formerly, Joel was the Vice President of ADAS, Business Development and Strategy at Magna Electronics, where he started the camera product line and grew that business to be the largest camera automotive tier-1 supplier globally. Joel has 15 patents and holds a BS in Systems Engineering from Oakland University.

Joel, in his distinct style, lays out the basics of mainstream location systems, their limitations, and how these are compensated. We then go on a whistle stop tour of Swift Navigation’s technology stack, and how it is making ADAS and AD applications possible with its high-precision positioning service.

This episode of the AI in Automotive podcast is slightly different from our usual episodes featuring interesting applications of AI in the automotive and mobility industries. In this episode, we are going a bit further up the data chain and exploring an interesting way in which high-quality data is made available to an AI algorithm.

If you find this episode interesting, do share it with your friends and colleagues and rate our podcast wherever you listen to it.

http://ai-in-automotive.com/aiia/112/joelgibson

AI in Automotive Podcast

AI-powered ADAS and AD systems are getting more complex with every iteration. New sensors, higher resolution cameras and increasingly sophisticated deep learning algorithms have substantial computational requirements. Traditionally, they have relied on compute resources in the cloud. But as systems get more advanced, relying on the cloud alone carries significant risk to systems that are supposed to work in real time under significant cost, space and power constraints. So how do we deal with this reality?

Enter Edge AI. An elegant solution that helps process part of the input from various sensors locally, rather than in the cloud. In other words, ‘at the edge’. 

In this episode of the AI in Automotive podcast, we are joined by Orr Danon, CEO of Hailo Technologies, a company that is bringing powerful edge AI solutions to the automotive industry. Hailo’s technology and processor have the capability to process several high-resolution video inputs in real time with low latency, without impacting the accuracy of the algorithm.

Orr and I dig deeper into what exactly edge AI is, what advantages it delivers and what its relevance is to ADAS and AD systems. We also talk about the future of autonomous driving, and discuss how the levels of autonomy might evolve in the future. 

I hope you enjoy our chat today as much as I did recording it with Orr. If you do, do give our podcast a shout on your social media, or share a link with your friends and colleagues.

AI in Automotive Podcast

If Elon Musk says something is hard, you can bet your bottom dollar that it is bloody hard. Automotive applications are some of the toughest, most unforgiving environments to deploy AI in. Most AI applications in automotive are mission critical - they just can’t go wrong. This requires that all the input they process is of a very high resolution or quality. They also need to operate in real-time, often perceive the environment around them and respond to it in a matter of milliseconds. Lastly, they need to operate with a very low power requirement, in an environment that is subject to dust, vibration and wide temperature variations. 

This intersection of constraints means that deep learning algorithms for automotive applications need to be designed right, and optimised constantly. This is where Deci comes in. Think of Deci as an easy-to-use platform that offers a set of tools to optimise your machine learning algorithms, and deliver order-of-magnitude performance improvements. 

In this episode of the AI in Automotive Podcast, I am joined by Yonatan Giefman, Co-founder & CEO of Deci. Yonatan tells us how AI applications are brought to life, and why they need to be optimised. We discuss the Deci platform, and how it can help data scientists and deep learning architects to optimise their algorithms, using the company’s proprietary neural algorithm search technology. We also talk about machines building machines, and even take a peek into science fiction :)

If you liked my chat with Yonatan today, do subscribe to the AI in Automotive podcast, and give us a shout on your social media.

https://www.ai-in-automotive.com/aiia/114/yonatangeifman

AI in Automotive Podcast

Have you ever thought about all the things that go on behind the scenes when you file an insurance claim for your car? Probably not. And that’s a good thing, because if you did have to think about the complexity that lies behind, you would not be a happy customer.

The reality, however, is that every insurance claim triggers hundreds of processes and transactions that work in harmony to verify and pay out your claim. The fascinating thing is that AI is starting to play an increasingly influential role in this ecosystem.

To understand this better, we invited Marc Fredman to the AI in Automotive Podcast. Marc is the Chief Strategy Officer at CCC Intelligent Solutions. CCC’s technology connects thousands of companies that make the insurance economy run and powers over a hundred billion dollars of insurance commerce.

In today’s conversation, Marc takes us inside the insurance economy, and shares with us the variety of ways in which artificial intelligence is helping power the insurance ecosystem.

If you like this episode of our podcast, do share it with your friends and colleagues, and rate us wherever you get your podcasts.

AI in Automotive Podcast