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 #machinelearninghttps://www.ai-in-automotive.com/aiia/305/garybrotmanAI 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

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 #autonomousdrivingAI 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/pauldryschAI 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

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

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 #softwaredefinedvehiclehttps://www.ai-in-automotive.com/aiia/206/hemantsikariaAI 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

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

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