AI in Automotive - #402 - Ben Rathaus - VP AI and Perception, Arbe Robotics

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

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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

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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

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.

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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

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.

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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

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 #autonomousdriving

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