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

#114 - Yonatan Geifman - Co-founder & CEO, Deci

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.

#113 - Orr Danon - CEO, Hailo Technologies

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

#112 - Joel Gibson - EVP of Automotive, Swift Navigation

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

#111 - Leslie Nooteboom - Co-founder & Chief Product Officer, Humanising Autonomy

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.

#110 - Arun Kumar - Managing Director, AlixPartners

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

#109 - Venkat Sreeram - Co-founder, ClearQuote

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.

#108 - Chris Van Dan Elzen - Vice President, Veoneer

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.

#107 - Noam Maital - CEO, Waycare Technologies

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.

#106 - Reinhard Stolle - VP Engineering, AID Autonomous Intelligent Driving

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.

#105 - Florian Baumann - CTO, Dell Technologies (Automotive & AI)

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.

#104 - Paul Eichenberg - Chief Strategist - Part 2

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.

#103 - Paul Eichenberg - Chief Strategist - Part 1

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.

#101 - Tom Wood - CEO, Cazana - Part 1

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 Trailer

The AI in Automotive podcast is a platform for conversations about the rapidly growing role of Artificial Intelligence and Machine Learning in the automotive and mobility industries. Host Jayesh Jagasia speaks to experts in the domain for free-wheeling conversations on how AI is shaping the future of the automotive industry.

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AI in Automotive

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