A new self-driving truck — manufactured by Volvo and loaded with autonomous vehicle tech developed by Aurora Innovation — could be on public highways as early as this summer. The Volvo VNL Autonomous truck, which was revealed Monday evening at the ACT Expo in Las Vegas, is the product of a partnership between Aurora and […]
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A new crop of early-stage startups — along with some recent VC investments — illustrates a niche emerging in the autonomous vehicle technology sector. Unlike the companies bringing robotaxis to city streets, these startups are taking their tech off-road. Two recent entrants — Seattle-based Overland AI and New Brunswick-based Potential — are poised to get […]
© 2024 TechCrunch. All rights reserved. For personal use only.
Wander the pits at any professional motorsports event, especially something like Formula 1, and you’ll see endless computer displays full of telemetry. Modern teams are awash in real-time digital feedback from the cars. I’ve been in many of these pits over the years and marveled at the streams of data, but never have I seen an instance of the Microsoft Visual Studio software development suite running there right amid the chaos.
But then, I’ve never attended anything like the inaugural Abu Dhabi Autonomous Racing League event this past weekend. The A2RL, as it is known, is not the first autonomous racing series: There’s the Roborace series, which saw autonomous race cars setting fast lap times while dodging virtual obstacles, and the Indy Autonomous Challenge, which most recently ran at the Las Vegas Motor Speedway during CES 2024.
While the Roborace focuses on single-car time trials and the Indy Autonomous series centers on oval action, A2RL set out to break new ground in a couple of areas.
A2RL put four cars on track, competing simultaneously for the first time. And, perhaps more significantly, it pitted the top-performing autonomous car against a human being, former Formula 1 pilot Daniil Kvyat, who drove for various teams between 2014 and 2020.
The real challenge was behind the scenes, with teams staffed with an impressively diverse cadre of engineers, ranging from fledgling coders to doctorate students to full-time race engineers, all fighting to find the limit in a very new way.
Unlike Formula 1, where 10 manufacturers design, develop and produce completely bespoke cars (sometimes with the help of AI), the A2RL race cars are entirely standardized to provide a level playing field. The 550-horsepower machines, borrowed from the Japanese Super Formula Championship, are identical, and the teams are not allowed to change a single component.
That includes the sensor array, which features seven cameras, four radar sensors, three lidar sensors and GPS to boot — all of which are used to perceive the world around them. As I would learn while wandering the pits and chatting to the various teams, not everybody is fully tapping into the 15 terabytes of data each car hoovers up every single lap.
Some teams, like the Indianapolis-based Code19, only started work on the monumental project of creating a self-driving car a few months ago. “There’s four rookie teams here,” said Code19 co-founder Oliver Wells. “Everyone else has been competing in competitions just like this, some of them for up to seven years.”
Munich-based TUM and Milan-based Polimove have extensive experience running and winning in both Roborace and the Indy Autonomous Challenge. That experience carries over, as does the source code.
“On the one hand, the code is continuously developed and improved anyway,” said Simon Hoffmann, team principal at TUM. The team made adjustments to change the cornering behavior to suit the sharper turns in the road course and also adjust the overtaking aggression. “But in general, I would say we use the same base software,” he said.
Through the series of numerous qualifying rounds throughout the weekend, the teams with the greatest experience dominated the timing charts. TUM and Polimove were the only two teams to complete lap times in less than two minutes. Code19’s fastest lap, however, was just over three minutes; the other new teams were far slower.
This has created a competition that’s rarely seen in software development. While there have certainly been previous competitive coding challenges, like TopCoder or Google Kick Start, this is a very different sort of thing. Improvements in code mean faster lap times — and fewer crashes.
Kenna Edwards is a Code19 assistant race engineer and a student at Indiana University. She brought some previous app development experience to the table, but had to learn C++ to write the team’s antilock braking system. “It saved us at least a couple of times from crashing,” she said.
Unlike traditional coding problems that might require debuggers or other tools to monitor, improved algorithms here have tangible results. “A cool thing has been seeing the flat spots on the tire improve over the next session. Either they’ve reduced in size or in frequency,” Edwards said.
This implementation of theory not only makes for engaging engineering challenges but also opens up viable career paths. After earlier interning with Chip Ganassi Racing and General Motors, and thanks to her experience with Code19, Edwards starts full-time at GM Motorsports this summer.
That sort of development is a huge part of what A2RL is about. Shadowing the main on-track action is a secondary series of competitions for younger students and youth groups around the world. Before the main A2RL event, those groups competed with autonomous 1:8-scale model cars.
“The aim is, next year, we keep for the schools the smaller model cars, we’ll keep for the universities maybe doing it on go-karts, a bit bigger, they can play with the autonomous go-karts. And then, if you want to be in the big league, you start racing on these cars,” said Faisal Al Bannai, the secretary general of Abu Dhabi’s Advanced Technology Research Council, the ATRC. “I think by them seeing that path, I think you’ll encourage more guys to come into research, to come into science.”
It’s Al Bannai’s ATRC that’s footing the bill for the A2RL, covering everything from the cars to the hotels for the numerous teams, some of whom have been testing in Abu Dhabi for months. They also put on a world-class party for the main event, complete with concerts, drone races, and a ridiculous fireworks show.
The on-track action was a little less spectacular. The first attempt at a four-car autonomous race was aborted after one car spun, blocking the following cars. The second race, however, was far more exciting, featuring a pass for the lead when the University of Modena’s Unimore team car went wide. It was TUM that made the pass and won the race, taking home the lion’s share of the $2.25 million prize purse.
As for man vs. machine, Daniil Kvyat made quick work of the autonomous car, passing it not once but twice to huge cheers from the assembled crowd of more than 10,000 spectators who took advantage of free tickets to come see a little bit of history — plus around 600,000 more streaming the event.
The technical glitches were unfortunate. Still it was a remarkable event to witness and illustrated how far autonomy has come — and of course, how much more progress needs to be made. The fastest car was still upward of 10 seconds off of Kvyat’s time. However, it ran smooth, clean laps at an impressive speed. That’s in stark contrast to the first DARPA Grand Challenge in 2004, which saw every single competitor either crashing into a barrier or meandering off into the desert on an unplanned sojourn.
For A2RL, the real test will be whether it can evolve into a financially viable series. Advertising drives most motorsports, but here, there’s the added benefit of developing algorithms and technologies that manufacturers could reasonably apply in their cars.
ATRC’s Al Bannai told me that while the series organizers own the cars, the teams own the code and are free to license it: “What they compete on at the moment is the algorithm, the AI algorithm that makes this car do what it does. That belongs to each of the teams. It doesn’t belong to us.”
The real race, then, might not be on the track, but in securing partnerships with manufacturers. After all, what better way to inspire confidence in your autonomous technology than by showing it can handle traffic on the race track at 160 mph?
Wayve, a UK-born startup developing a self-learning rather than rule-based system for autonomous driving, has closed a $1.05 billion in Series C funding led by SoftBank Group. This is the UK’s largest AI fundraise ever and sits among the top 20 AI fundraises globally to date.
Also participating in the raise was NVIDIA and existing investor Microsoft. Waye’s early stage investors included Meta’s head of AI, Yann LeCun.
Wayve, which was founded in Cambridge in 2017, raised $200m in a series B round in January last year led by Eclipse Ventures.
The company plans to use the fresh capital injection to develop its product for “eyes on” assisted driving and “yes off” fully automated driving, other AI-assisted automotive applications, and expand operations globally.
San Francisco has become known as the epicenter for autonomous driving roll-outs, with Alphabet-owned Waymo and GM-owned Cruise both operating services in the city. By contrast, Wayve’s “end-to-end” self-driving system began its life around the tiny streets of Cambridge on an electric Renault Twizy.
Since then, it has been training its model on delivery vehicles for the likes of companies like UK grocery delivery company Ocado, which invested $13.6 million in the startup.
Wayve’s approach to autonomous driving is similar to Tesla’s, but Wayve plans to sell its autonomous driving model to a variety of auto OEMs. The implication, of course, is that Wayve will garner a great deal more training data on which to improve its model, as Tesla must rely on someone buying their car brand. The company has not announced any such automotive partners yet, however.
Wayve calls its hardware-agnostic mapless product an “Embodied AI”, and it plans to distribute its platform not just to car makers but also to robotics companies serving manufacturers of all descriptions, allowing the platform to learn from human behavior in a wide variety of real-world environments. The company’s research on multimodal and generative models, known as LINGO and GAIA, will offer “language-responsive interfaces, personalized driving styles, and co-piloting,” the firm promises.
Wayve co-founder and CEO Alex Kendall told Techcrunch: “Seven years ago, we started the company to go build an embodied AI. We have been heads down building technology … What happened last year was everything really started to work.”
He said the key moment has been the automotive industry’s “step change” into having cameras surrounding new cars, from which Wayve can draw data for its autonomous platform: “Now their production vehicles are coming out with GPUs, surrounding cameras, radar, and of course the appetite to now bring AI onto, and enable, an accelerated journey from assisted to automated driving. So this fundraise is a validation of our technological approach, and gives us the capital to go and turn this technology into product and bring this this this product to market.”
He added that Wayve has big plans for robotics as well.
“Very soon you’ll be able to buy a new car, and it’ll have Wayve’s AI on it… Then this goes into enabling all kinds of embodied AI, not just cars, but other forms of robotics. I think the ultimate thing that we want to achieve here is to go way beyond where AI is today with language models and chatbots. But to really enable a future where we can trust intelligent machines that we can delegate tasks to, and of course they can enhance our lives and self-driving will be the first example of that.”
In a move that signified the importance of this fundraise more broadly to the UK, Prime Minister Rishi Sunak issued a supporting statement saying: “From the first electric light bulb or the World Wide Web, to AI and self-driving cars – the UK has a proud record of being at the forefront of some of the biggest technological advancements in history.”
“I’m incredibly proud that the UK is the home for pioneers like Wayve who are breaking ground as they develop the next generation of AI models for self-driving cars. The fact that a homegrown, British business has secured the biggest investment yet in a UK AI company is a testament to our leadership in this industry, and that our plan for the economy is working,” he said.
“We are leaving no stone unturned to create the economic conditions for businesses to grow and thrive in the UK. We already have the third highest number of AI companies and private investment in AI in the world, and this announcement anchors the UK’s position as an AI superpower,” he added.
Also in a statement, Kentaro Matsui, Managing Partner at SoftBank Investment Advisers and a Wayve boardmember said: “AI is revolutionizing mobility… The potential of this type of technology is transformative; it could eliminate 99% of traffic accidents. SoftBank Group is delighted to be at the forefront of this effort with Wayve, as advanced intelligence redefines mobility and connectivity, contributing to a more convenient and safer society.”
Hyundai has agreed to spend nearly $1 billion on Motional, an investment that will give the automaker a majority stake while providing the self-driving startup with the necessary capital to keep operating.
The Korean automaker invested $475 million directly into Motional as part of a broader deal that includes buying out joint venture partner Aptiv. As part of the deal, Hyundai will spend another $448 million to buy 11% of Aptiv’s common equity interest in Motional, according to information revealed Thursday in Aptiv’s first-quarter earnings report.
Aptiv also shared that it expects to reduce its common equity interest in Motional from 50% as of March 31 to about 15%, leaving Hyundai with the remaining 85% control. Aptiv Chairman and CEO Kevin Clark flagged in January that the company would reduce its ownership interest in Motional. The company said at the time that it would stop allocating capital towards Motional due to the high cost of commercializing a robotaxi business and the long road ahead to profits.
Aptiv on Thursday reduced its full-year net sales forecast for 2024 to be between $20.85 billion and $21.45 billion, down from between $21.3 billion to $21.9 billion.
Motional confirmed the new funding round and increased stake from Hyundai. The company did not respond to TechCrunch’s inquiry regarding the accuracy of Aptiv’s figures. Hyundai could also not be reached for confirmation.
Motional started as Boston-based autonomous vehicle startup nuTonomy in 2013, before being acquired by Delphi for $400 million. Delphi would later split it’s business with the Aptiv unit absorbing nuTonomy. The entity became Motional under a $4 billion Hyundai-Aptiv joint venture in 2019. While it’s clear from Aptiv’s earnings report that the company is trying to manage risks and optimize finances amid a less positive outlook, the company’s retreat, and Hyundai’s step forward, raises questions about Motional’s future.
In March, TechCrunch reported that Motional secured a bridge loan for an undisclosed amount as a lifeline while the AV startup secured its next round of longer-term funding. While it’s likely that this funding round from Hyundai fits that bill, Motional has not responded to TechCrunch’s request for more information about whether it will need to acquire more investors in the future.
Motional has been testing its autonomous vehicles with a safety driver behind the wheel in Boston, Pittsburgh, Las Vegas, Los Angeles and Singapore. The company’s go-to-market strategy involves partnering with existing ride-hail platforms like Uber, Lyft and Via to give customers rides. Motional has stated its goal of launching a robotaxi service using driverless Hyundai Ioniq 5 vehicles in 2024.
Motional and Hyundai announced plans in November 2023 to co-develop production-ready versions of the all-electric Ioniq 5 robotaxi at the automaker’s new innovation center in Singapore, the Hyundai Motor Group Innovation Center Singapore (HMGICS). During CES 2024, Motional also announced plans to work with Kia on a next-generation vehicle that will enter commercial operations later this decade, with initial development stages beginning this year.
Motional’s financial shifts come as the robotaxi industry continues to face uncertainty. The startup has been inching slowly towards commercialization, launching pilots in at least five cities. Crucially, Motional has not yet begun charging for rides or deliveries yet. Meanwhile among the competition, Waymo continues to expand its fully driverless, paid robotaxi service in San Francisco, Los Angeles and Phoenix, with plans to hit Austin later this year. GM’s Cruise is still mainly off the streets after an incident in October 2023 that left a pedestrian stuck under and dragged by one of its robotaxis, but the company has begun mapping again in Phoenix as part of a slow, deliberate reintroduction to public roads.
Then there’s Tesla. CEO Elon Musk has shaken up his company, laying off thousands and increasing investment into AI, in a stated goal to go “balls to the walls for autonomy” and deliver a robotaxi in August.
Wander the pits at any professional motorsports event, especially something like Formula 1, and you’ll see endless computer displays full of telemetry. Modern teams are awash in real-time digital feedback from the cars. I’ve been in many of these pits over the years and marveled at the streams of data, but never have I seen an instance of the Microsoft Visual Studio software development suite running there right amid the chaos.
But then, I’ve never attended anything like the inaugural Abu Dhabi Autonomous Racing League event this past weekend. The A2RL, as it is known, is not the first autonomous racing series: There’s the Roborace series, which saw autonomous race cars setting fast lap times while dodging virtual obstacles; and the Indy Autonomous Challenge, which most recently ran at Las Vegas Motor Speedway during CES 2024.
While the Roborace focused on single-car time trials and the Indy Autonomous series centers on oval action, A2RL set out to break new ground in a couple of areas.
A2RL put four cars on track, competing simultaneously for the first time. And, perhaps more significantly, it pitted the top-performing autonomous car against a human being, former Formula 1 pilot Daniil Kvyat, who drove for various teams between 2014 and 2020.
The real challenge was behind the scenes, with teams staffed with an impressively diverse cadre of engineers, ranging from fledgling coders to doctorate students to full-time race engineers, all fighting to find the limit in a very new way.
Unlike Formula 1, where 10 manufacturers design, develop and produce completely bespoke cars (sometimes with the help of AI), the A2RL race cars are entirely standardized to provide a level playing field. The 550-horsepower machines, borrowed from the Japanese Super Formula Championship, are identical, and the teams are not allowed to change a single component.
That includes the sensor array, which features seven cameras, four radar sensors, three lidar sensors and GPS to boot — all of which are used to perceive the world around them. As I would learn while wandering the pits and chatting to the various teams, not everybody is fully tapping into the 15 terabytes of data each car hoovers up every single lap.
Some teams, like the Indianapolis-based Code 19, only started work on the monumental project of creating a self-driving car a few months ago. “There’s four rookie teams here,” said Code 19 co-founder Oliver Wells. “Everyone else has been competing in competitions just like this, some of them for up to seven years.”
Munich-based TUM and Milan-based Polimove have extensive experience running and winning in both Roborace and the Indy Autonomous Challenge. That experience carries over, as does the source code.
“On the one hand, the code is continuously developed and improved anyway,” said Simon Hoffmann, team principal at TUM. The team made adjustments to change the cornering behavior to suit the sharper turns in the road course and also adjust the overtaking aggression. “But in general, I would say we use the same base software,” he said.
Through the series of numerous qualifying rounds throughout the weekend, the teams with the greatest experience dominated the timing charts. TUM and Polimove were the only two teams to complete lap times in less than two minutes. Code 19’s fastest lap, however, was just over three minutes; the other new teams were far slower.
This has created a competition that’s rarely seen in software development. While there have certainly been previous competitive coding challenges, like TopCoder or Google Kick Start, this is a very different sort of thing. Improvements in code mean faster lap times — and fewer crashes.
Kenna Edwards is a Code 19 assistant race engineer and a student at Indiana University. She brought some previous app development experience to the table, but had to learn C++ to write the team’s antilock braking system. “It saved us at least a couple of times from crashing,” she said.
Unlike traditional coding problems that might require debuggers or other tools to monitor, improved algorithms here have tangible results. “A cool thing has been seeing the flat spots on the tire improve over the next session. Either they’ve reduced in size or in frequency,” Edwards said.
This implementation of theory not only makes for engaging engineering challenges but also opens up viable career paths. After earlier interning with Chip Ganassi Racing and General Motors, and thanks to her experience with Code 19, Edwards starts full-time at GM Motorsports this summer.
That sort of development is a huge part of what A2RL is about. Shadowing the main on-track action is a secondary series of competitions for younger students and youth groups around the world. Before the main A2RL event, those groups competed with autonomous 1:8-scale model cars.
“The aim is, next year, we keep for the schools the smaller model cars, we’ll keep for the universities maybe doing it on go-karts, a bit bigger, they can play with the autonomous go-karts. And then, if you want to be in the big league, you start racing on these cars,” said Faisal Al Bannai, the secretary general of Abu Dhabi’s Advanced Technology Research Council, the ATRC. “I think by them seeing that path, I think you’ll encourage more guys to come into research, to come into science.”
It’s Al Bannai’s ATRC that’s footing the bill for the A2RL, covering everything from the cars to the hotels for the numerous teams, some of whom have been testing in Abu Dhabi for months. They also put on a world-class party for the main event, complete with concerts, drone races, and a ridiculous fireworks show.
The on-track action was a little less spectacular. The first attempt at a four-car autonomous race was aborted after one car spun, blocking the following cars. The second race, however, was far more exciting, featuring a pass for the lead when the University of Modena’s Unimore team car went wide. It was TUM that made the pass and won the race, taking home the lion’s share of the $2.25 million prize purse.
As for man vs. machine, Daniil Kvyat made quick work of the autonomous car, passing it not once but twice to huge cheers from the assembled crowd of more than 10,000 spectators who took advantage of free tickets to come see a little bit of history — plus around 600,000 more streaming the event.
The technical glitches were unfortunate. Still it was a remarkable event to witness and illustrated how far autonomy has come — and of course, how much more progress needs to be made. The fastest car was still upwards of 10 seconds off of Kvyat’s time. However, it ran smooth, clean laps at an impressive speed. That’s in stark contrast to the first DARPA Grand Challenge in 2004, which saw every single competitor either crashing into a barrier or meandering off into the desert on an unplanned sojourn.
For A2RL, the real test will be whether it can evolve into a financially viable series. Advertising drives most motorsports, but here, there’s the added benefit of developing algorithms and technologies that manufacturers could reasonably apply in their cars.
ATRC’s Al Bannai told me that while the series organizers own the cars, the teams own the code and are free to license it: “What they compete on at the moment is the algorithm, the AI algorithm that makes this car do what it does. That belongs to each of the teams. It doesn’t belong to us.”
The real race, then, might not be on the track, but in securing partnerships with manufacturers. After all, what better way to inspire confidence in your autonomous technology than by showing it can handle traffic on the race track at 160 mph?
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