Aston Martin Has No Plans To Develop Autonomous Technology

Amen.

Everywhere you look these days it’s as if every automaker is busy at work developing autonomous technologies, at least to an extent. Not Aston Martin. The iconic English brand is currently in something of a product renaissance, specifically the all-new DB11, the insane Vulcan and upcoming Valkyrie hypercar, as well as its just announced racing-inspired AMR brand. We were fortunate to speak with Vice President and Chief Special Operation Officer David King at Geneva last week, and were reminded once again why we love Aston Martin.

“Our customers want more track-focused cars, specifically Porsche 911 GT3 competitors, and therefore we will offer our cars with two levels of potency: Heavily re-engineered AMR Pro cars, such as the Vantage AMR Pro, and the first tier AMR, like the Rapide AMR.” Knowing this, we asked King if Aston Martin is working on new technologies that don’t necessarily involve performance, such as self-driving tech. “No,” came the immediate reply with a smile. “We are not working in-house on autonomous technology. This doesn’t mean that one day perhaps we’ll license someone else’s technology. But, for me, Aston Martin is about driving passion and performance, and that’s what we’re focused on right now,” King explained.

Another very promising sign of Aston Martin’s revival of sorts was the arrival of Dr. Andy Palmer as CEO. After a very successful career at Nissan, Palmer is certainly the right man for this job. “He asks how we can share Aston Martin racing with a wider audience. He wants engineers to think like entrepreneurs, and gives more freedom to spend on exotic materials,” stated King. We also took a stab and asked King about the future of the naturally aspirated engine Aston Martin is currently phasing out. “The naturally aspirated engine is limited by how far you can rev it to make power.”

Along with increasing global emission standards, it’s fair to say the NA engine, even for Aston Martin, is about to retire, replaced not only by turbocharging, but also hybrids and electrics. So rest assured. We won’t be seeing self-driving technology coming from these guys anytime soon, and if we read King correctly, he’s in absolutely no rush to even buy the tech from elsewhere.

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2019 Audi A8 Won't Have Level 3 Autonomous Tech In The US

US pricing has also been announced for Audi's new flagship sedan.

Audi made a big deal about the new A8 being the first production model to feature Level 3 semi-autonomous driving tech when it debuted in LA last year. Using a combination of a laser scanner, radar sensors, a front camera and ultrasonic sensors, the A8’s highly-touted Traffic Jam Pilot enables the A8 to drive itself at speeds of up to 37.3 mph on freeways and highways. Unlike Tesla’s Autpilot, the driver doesn’t need to keep their hands on the wheel but must be able to retake control if required.

Unfortunately, if you were looking forward to enjoying some hands-free driving in the A8 we’re afraid you’re out of luck because the semi-autonomous tech won’t be offered in the US. According to Roadshow, Traffic Jam Pilot won’t be available in America due to “infrastructural and consumer issues” as well as a “quagmire of legal” problems. To be fair, Audi has always been clear the technology will only be rolled out in markets where national regulations permit it, but the flagship sedan has still lost one of its main unique selling points. Instead, the US-spec Audi A8 will feature a hands-on Level 2 adaptive cruise control system with steering, acceleration and full braking support.

Audi has also announced how much the new flagship sedan will cost when it goes on sale in the US. Arriving in dealerships later this fall, the 2019 Audi A8 starts at $83,800 excluding destination and delivery fees, making it $1,300 more expensive than the 2018 model. For that price, the A8 is very generously equipped. Standard equipment includes adaptive air suspension, leather upholstery and 18-way power front seats. Inside, you also get a 12.3-inch digital instrument cluster, and the latest version of Audi's MMI infotainment system. This includes two screens: a 10.3-inch screen providing quick views at maps and audio, and a 8.6-inch lower screen that lets you change the climate control settings and seat features.

At launch, the A8 will be offered exclusively with a turbocharged 3.0-liter V6 engine sending 335 hp and 369 lb-ft of torque to all four wheels through an eight-speed automatic transmission. 0-62 mph takes 5.7 seconds before the luxury sedan hits a top speed of 155 mph. According to Roadshow, a V8 version of the A8 will also be rolled out next summer, though it’s unclear if the publication is referring to the S8, which is expected to use a twin-turbo 4.0-liter V8 with around 530 hp, or a new V8-powered mainstream A8.

VW to launch autonomous Sedric first in U.S.

Volkswagen plans to roll out its Sedric self-driving car in the U.S.

The European auto industry has a fear when it comes to the advent of autonomous driving.

It's that Europe is falling behind in the new technology race, losing the competitive advantage to companies in the U.S. and China, where advanced research is occurring faster and more freely.

Johann Jungwirth, for one, is visibly frustrated.

Volkswagen Group's chief digital officer knows that automotive companies are locked in a battle with tech companies for leadership in autonomous driving.

Jungwirth, a former Apple engineer, fears European regulations are hampering efforts to bring VW's battery-powered Sedric concept to market. VW management has decided to move Sedric, short for "self-driving car," into production. But instead of in VW's home market of Germany, it will launch first in the U.S.

"My goal is to be in the first U.S. cities with driverless cars in 2021," Jungwirth said as he presented the latest iteration of the car in Hanover, Germany. After that will come a rollout in China, Singapore and in Middle Eastern cities such as Dubai. "And then comes Europe. We would love to come earlier since it's our home market, but the legislation just isn't there."

European leaders and consumers are well aware of the potential of autonomous driving. Every year, half a million metric tons of carbon dioxide emissions in Germany alone could be saved by eliminating the search for parking spaces, which studies show account for up to 30 percent of inner-city traffic. Every year 1.25 million people die around the world, and as many as 50 million are injured in road traffic accidents, according to United Nations statistics. Every year children, the elderly or the physically challenged have little or no access to individual mobility.

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VW's Sedric and others like it could change that. Vehicles legally permitted to operate without any driver behind the wheel would revolutionize transportation.

But there are challenges.

Roadblocks

Carmakers looking to test vehicles such as the Sedric in small-scale pilot operations prefer California over Europe — at least initially. Many carmakers have teams of engineers around Silicon Valley, the population is tech-savvy and open to innovation, the streets are much wider, weather conditions are usually ideal and the state government supports them.

By comparison, one major roadblock in Europe is the United Nations Economic Commission for Europe, a standards-setting body responsible for regulating the homologation and use of motor vehicles. Roughly 60 countries participate in the oversight of European traffic, and no consensus has been reached over the rollout of self-driving vehicles. Regulators are focused on more gradual innovations.

Virtually the entire continent is governed by the U.N.'s Vienna Convention on Road Traffic. This largely restricts the use of self-driving vehicles on public roads to limited testing scenarios, and legalizing their commercial operation is years from becoming reality.

"We really have a competitive disadvantage because of the UNECE," said Jungwirth, who worries that those late to the market might end up fighting over the scraps left behind. "The winner could take it all."

No alignment

Responding to the criticism, the UNECE introduced a procedure that lets companies apply for a special exemption for a vehicle such as the Sedric. This is now subject to debate among UNECE member states, however, meaning the outcome of negotiations is uncertain and at least a four-fifths majority would need to vote in favor.

Neither the U.S. nor China has aligned its laws with European regulations on road traffic or type approval. This allows those nations to respond faster to technological advances, but also creates a patchwork of regulatory environments for carmakers.

"Progress must not stop at national borders," argued Daimler board member Renata Jungo Brüngger. "Legislation must keep up with technical progress, otherwise paramount innovations for automated and autonomous driving cannot be brought to the road."

$65 billion

Fearing cash-rich tech companies will capitalize on their expertise in artificial intelligence and machine learning, most major carmakers are spending heavily to keep pace.

Renault unveiled its EZ-GO concept in Geneva that could form the basis for a self-driving vehicle fleet. Daimler plans to pilot a highly autonomous vehicle fleet in California in the second half of next year. Rival BMW looks to test in China and has formed a consortium around Intel and Mobileye.

All hope to bring the technology to market early next decade. Between tech and auto companies, AlixPartners estimates some $65 billion will be invested this year, up nearly tenfold from 2015.

In the lead, however, is industry pioneer Waymo, a spinoff from Google that has racked up 7 million miles of testing on public roads since it started nine years ago.

Now ready to launch its own branded mobility service starting this year in Arizona and California, Waymo is looking to branch out. With an eye toward Europe, it demonstrated its prototype Chrysler Pacifica in Italy in early June during an investor day held by industrial partner Fiat Chrysler Automobiles.

"There are differences in the regulatory and policy environment that are really important, very different from what we're facing in the U.S., but there is also an opportunity for us to experiment here in Europe," Waymo CEO John Krafcik told the Automotive News Europe Congress in Turin.

But VW may have one clear advantage as it tries to narrow Waymo's lead. Neither Waymo nor its partner, FCA, has a purpose-built self-driving vehicle on the horizon. Public acceptance of the technology is important, and the perception of security could be a key competitive advantage.

The Sedric, with its sturdy, monolithic appearance, gives occupants the feeling they are safely ensconced in a vehicle impervious to damage. Waymo, meanwhile, has to make do with a bulky laser scanner on the roof, which Krafcik says assures passengers concerns through an outward symbol of the vehicle's intelligence.

Krafcik downplayed the need for vehicles such as Sedric that need to master all situations.

"We're pretty skeptical on Level 5," he said, adding. "It will take decades and I don't even think it's necessary."

Jungwirth disagrees.

"The technology is almost ready. I would love to see the legislation support us," he said. "Testing is fine, but what we need is commercial operation in order to scale up."

View the original article here

Why no auto executive will temper autonomous vehicle expectations

Even while his company tested a self-driving vehicle at the 2017 CAR Management Briefing Seminars, Magna CEO Don Walker tempered expectations about the technology. “A full autonomous vehicle is a long way off for lots of reasons," he said. Photo credit: GREG HORVATH

I've been hoping this summer that someone somewhere at an automaker or supplier would be brave enough to say that autonomous vehicles -- which still have no path to profitability and still have no clear customer base clamouring for them -- aren't going to happen anytime soon.

I don't recall anyone saying it last week at the CAR Management Briefing Seminars in Traverse City, Mich. I haven't heard it recently on the record from auto company executives and suppliers, both of whom are heavily invested in trying to bring about self-driving vehicles, even if they harbor personal doubts. And some do.

After interviews, when the tape recorder is off and there is time for off-the-record chitchat, several executives have expressed doubts on the technology, the timing, the readiness of the infrastructure or consumer willingness to pay for the technology as new-vehicle prices are soaring. Last month, according to Kelley Blue Book, the average new light vehicle in the U.S. sold for US$35,359.

But automakers and suppliers are in a tough spot.

If Wall Street analysts perceive that a company is falling behind competitors, shares will get hammered. A year ago at the CAR conference, Magna CEO Don Walker, in unusually candid remarks for a senior executive, offered some insight into what industry executives say on the record and what they really think about self-driving vehicles.

He said: "Quite frankly, auto companies can't tell publicly what they really believe. They know what's going to happen, but they have to say what is going to be popular to be perceived as a progressive company. ... We've got a lot of feedback from many of the car companies, and they actually believe this to be right."

“A full autonomous vehicle is a long way off for lots of reasons," he added.?

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But the big money being spent trying to make a vehicle drive itself with 100 percent safety 100 percent of the time is -- in my view -- starting to damage some car companies' ability to compete.

Take Ford, for example. The F-series truck is Ford's perennial cash cow and the vehicle that pretty much pays all the big bills. If something happened -- an earthquake that disrupts supply, a huge spike in material costs -- and F-series margins disappeared, Ford would be in trouble fast. A fire in May at a supplier cost Ford eight days of production of the F series, a vehicle that is expected to bring in around US$44 billion in revenue this year.

Ford plans to invest US$4 billion in self-driving vehicles by 2023. But nowhere in Ford's announcement of that big number can I find when it expects to make a profit from the technology.

One lesson that never seems to resonate is that full-line automakers need a balanced portfolio of vehicles with an array of powertrains. And like a financial portfolio, some of those investments will make money and some won't. But to hedge your bets, you keep at least a few of the unprofitable ones around -- cars, for instance -- because things can change in a minute in the auto industry. Remember US$4 a gallon gasoline a decade ago? Or about $1.40 a litre in Canada at the time.

I am not saying automakers shouldn't spend money on self-driving technology. But autonomous vehicles should be about 10th on their priority lists. We live in a fast-changing world of tightening emissions regulations, burgeoning tariffs, a market shifting rapidly away from traditional cars, financial analysts with too much influence and other pressing issues.

There also needs to be more global collaboration among automakers, suppliers, regulators and everyone else with skin in the game to develop the key technologies to ensure that every vehicle on the road has the same basic building blocks. That would cut down on redundant engineering, improve safety, reduce costs, speed development and keep engineers where they are needed most: developing products that pay the bills today.

Ultimately, the industry needs to lower expectations. There still are too many unknowns about the practical application of self-driving vehicles. We don't even have national legislation governing the use of these vehicles in the U.S.

If we are going to be honest about self-driving cars, let's agree the best shot these vehicles have for even limited use in the next 15 years will be in geofenced areas, universities, hospital campuses, military bases and amusement park parking lots -- places where traffic is controlled, consistent and predicable.

This is one race where finishing first just means you are going to lose more money faster.

View the original article here

Tesla prefers custom chip for autonomous vehicles

Elon Musk's Tesla is developing its own self-driving chip rather than using someone else's, such as Nvidia's Drive Xavier, at left.

Bombshells are a common occurrence in Tesla's quarterly analyst calls, and the latest was no exception. As soon as CEO Elon Musk introduced the call, he turned the microphone over to members of his Autopilot team who announced that Tesla had spent three years developing a custom "neural network accelerator" chip that is now nearly ready to power its upcoming autonomous hardware suite.

According to Pete Bannon, Tesla's director of Autopilot hardware engineering, Tesla already has drop-in chip replacements for the Model S, X, and 3. "The chips are up and working," he says. "All have been driven in the field."

If you are not neck-deep in the world of autonomous vehicle chip design, this may not seem like a big deal. But for people in the know, such as executives at chip makers Nvidia and Intel, Tesla's announcement makes it clear the company thinks it can make bigger advances in self-driving cars on its own.

Tesla's development of a new chip specifically for its self-driving hardware is the latest example of the firm's commitment to vertical integration, meaning it makes a lot of components in its own factories, including Tesla seats. Currently Tesla uses Nvidia Drive PX2 boards in its vehicles. Just two years ago, Musk hailed Nvidia's boards as "basically a supercomputer in a car."

"Nvidia's complete platform is of course a powerful system, built to automotive grade, but it may not be perfect for what Tesla wants to use it for," says Mike Ramsey, automotive research director at Gartner. "Probably more important, Elon and Tesla feel like they need to own this technology. If they think the chip vendors are slowing them down, or locking them into a certain architecture or into a long-term design from which they cannot easily escape, then building your own chip makes some sense."

Unclear benchmarks

Bannon says the new chip is "a bottom-up design" optimized for the neural net algorithms that Tesla uses in its Autopilot driver-assistance system and in its long-promised "full self-driving" option. The chip is the third iteration of its Autopilot hardware, which his team designed.

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By building the chip itself, Bannon says, Tesla can create self-driving hardware that is "dramatically more efficient and has dramatically more performance than what you can buy today."

It's unclear whether Tesla's benchmarks for chip performance match with the rest of the industry. Musk says the new chip can process 2,000 frames of sensor data a second, compared with the current Nvidia chip, which can process 200 frames a second.

Nvidia says those claims are not fair. Danny Shapiro, senior director of automotive for Nvidia, says Musk is comparing Tesla's chip with Nvidia's 3-year-old Drive PX2.

"A more accurate comparison would have been to our current generation, Drive Xavier, which was designed from the ground up to be an autonomous vehicle processor," Shapiro says.

Nvidia has become the leading source of processors for self-driving neural net algorithms, which run more efficiently on the firm's graphics processing unit, or GPU, architecture than traditional central processing units, or CPUs. The company supplies Toyota, Volkswagen, Volvo, BMW, Daimler, Honda, Renault-Nissan, Bosch, Baidu and others.

Musk said Tesla figured out what was slowing down the Nvidia Drive PX2 board: There was a bottleneck between the CPU and GPU.

But Shapiro said Nvidia already figured that out and saw a tenfold improvement in data processing when it tested the Drive Xavier boards. Bandwidth improved from 2 gigabytes per second to 20 gigabytes per second. Xavier is also Nvidia's most efficient automated driving board to date, achieving 30 trillion operations per second, or TOPS, with just 30 Watts, compared with Drive PX2's peak of 24 TOPS at 150 Watts. Tesla's custom version of the PX2 produces between 8 and 10 TOPS.

Next year, Nvidia will make a board called Drive Pegasus publicly available, which integrates two Xavier chips each with current Volta-generation integrated GPUs and adds two next-generation discrete GPUs as well as two deep-learning accelerators, for a staggering 320 TOPS at 500 Watts.

"Our performance has gone up by more than a factor of 10, generation over generation," Shapiro says

Just as importantly, Shapiro says, Nvidia has been making sure that its performance gains don't come at the expense of flexibility.

"Development of these neural nets is so new and is changing so rapidly … if you lock in a particular type of neural network you have no flexibility to take advantage of these innovations," Shapiro said.

Intel's approach

Nvidia's archrival Intel takes an approach closer to Tesla's, co-developing processors that are optimized for integrated software applications.

"We do software-hardware co-design," says Jack Weast, Intel's chief systems architect of autonomous driving solutions. "We let the needs of the software algorithm drive what goes into the hardware. You can do a much, much more efficient implementation of portions of an algorithm if you know what that algorithm is in advance."

Intel's recent acquisition of the Israeli automotive computer vision company Mobileye, whose EyeQ3 chip powered Tesla's first generation of Autopilot hardware, gives Intel a significant head start, Weast says.

"Unlike some companies who are delivering their first deep-learning accelerator chip to market, we're actually on our third generation," he says. The latest chip, called EyeQ5, will start appearing in cars on the road in 2019. A recent Reuters report said the chip will be in as many as 8 million automated vehicles starting in 2021.

Intel and Nvidia's different approaches highlight how divergent autonomous vehicle development strategies can be, with some automakers seeking an efficiently optimized hardware-software package like Intel's, and others preferring the raw power and flexibility of Nvidia's chips and boards.

The history of Tesla's relationships with both companies suggests that it bridles at both, having publicly complained about the limitations of both Mobileye's relatively mature products as well as the relative inefficiency of Nvidia's.

Tesla's preferences

Perhaps the biggest question about Tesla's move toward more specialized silicon is whether it has really reached a point of software maturity where it makes sense to start optimizing its hardware. And even if it has, there are questions about its ability to keep pace with the powerhouse firms that dedicate massive r&d budgets to continuously improving their offerings.

"Tesla is not a giant chip company," Ramsey says. "Nvidia is spending billions of dollars investing in this technology, mostly subsidized by its incredibly healthy video game business. Intel, similarly, can pour massive resources into the design and validation of the chips. They both either own or have good relationships with huge chip manufacturers. Tesla is unlikely to save money and could produce a product that doesn't perform as well in the field."

Some scrappier startups are rethinking the way chips are placed in the vehicle, putting deep-learning chips near the sensors rather than near the centralized stack. Orr Danon, founder and CEO of one such company called Hailo Technologies, sees great opportunities for "fresh thinking about how we imagine a computer operating" in the autonomous vehicles of the future. But, he warns, there are challenges of trying to prematurely sell rapidly changing cutting-edge technologies.

"This is an exciting and essential step, but we all have to be aware that the road ahead to a stable technology is long, and do our best to understand how to make the overall path as smooth as possible," he says.

View the original article here

Tesla prefers custom chip for autonomous vehicles

Elon Musk's Tesla is developing its own self-driving chip rather than using someone else's, such as Nvidia's Drive Xavier, at left.

Bombshells are a common occurrence in Tesla's quarterly analyst calls, and the latest was no exception. As soon as CEO Elon Musk introduced the call, he turned the microphone over to members of his Autopilot team who announced that Tesla had spent three years developing a custom "neural network accelerator" chip that is now nearly ready to power its upcoming autonomous hardware suite.

According to Pete Bannon, Tesla's director of Autopilot hardware engineering, Tesla already has drop-in chip replacements for the Model S, X, and 3. "The chips are up and working," he says. "All have been driven in the field."

If you are not neck-deep in the world of autonomous vehicle chip design, this may not seem like a big deal. But for people in the know, such as executives at chip makers Nvidia and Intel, Tesla's announcement makes it clear the company thinks it can make bigger advances in self-driving cars on its own.

Tesla's development of a new chip specifically for its self-driving hardware is the latest example of the firm's commitment to vertical integration, meaning it makes a lot of components in its own factories, including Tesla seats. Currently Tesla uses Nvidia Drive PX2 boards in its vehicles. Just two years ago, Musk hailed Nvidia's boards as "basically a supercomputer in a car."

"Nvidia's complete platform is of course a powerful system, built to automotive grade, but it may not be perfect for what Tesla wants to use it for," says Mike Ramsey, automotive research director at Gartner. "Probably more important, Elon and Tesla feel like they need to own this technology. If they think the chip vendors are slowing them down, or locking them into a certain architecture or into a long-term design from which they cannot easily escape, then building your own chip makes some sense."

Unclear benchmarks

Bannon says the new chip is "a bottom-up design" optimized for the neural net algorithms that Tesla uses in its Autopilot driver-assistance system and in its long-promised "full self-driving" option. The chip is the third iteration of its Autopilot hardware, which his team designed.

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By building the chip itself, Bannon says, Tesla can create self-driving hardware that is "dramatically more efficient and has dramatically more performance than what you can buy today."

It's unclear whether Tesla's benchmarks for chip performance match with the rest of the industry. Musk says the new chip can process 2,000 frames of sensor data a second, compared with the current Nvidia chip, which can process 200 frames a second.

Nvidia says those claims are not fair. Danny Shapiro, senior director of automotive for Nvidia, says Musk is comparing Tesla's chip with Nvidia's 3-year-old Drive PX2.

"A more accurate comparison would have been to our current generation, Drive Xavier, which was designed from the ground up to be an autonomous vehicle processor," Shapiro says.

Nvidia has become the leading source of processors for self-driving neural net algorithms, which run more efficiently on the firm's graphics processing unit, or GPU, architecture than traditional central processing units, or CPUs. The company supplies Toyota, Volkswagen, Volvo, BMW, Daimler, Honda, Renault-Nissan, Bosch, Baidu and others.

Musk said Tesla figured out what was slowing down the Nvidia Drive PX2 board: There was a bottleneck between the CPU and GPU.

But Shapiro said Nvidia already figured that out and saw a tenfold improvement in data processing when it tested the Drive Xavier boards. Bandwidth improved from 2 gigabytes per second to 20 gigabytes per second. Xavier is also Nvidia's most efficient automated driving board to date, achieving 30 trillion operations per second, or TOPS, with just 30 Watts, compared with Drive PX2's peak of 24 TOPS at 150 Watts. Tesla's custom version of the PX2 produces between 8 and 10 TOPS.

Next year, Nvidia will make a board called Drive Pegasus publicly available, which integrates two Xavier chips each with current Volta-generation integrated GPUs and adds two next-generation discrete GPUs as well as two deep-learning accelerators, for a staggering 320 TOPS at 500 Watts.

"Our performance has gone up by more than a factor of 10, generation over generation," Shapiro says

Just as importantly, Shapiro says, Nvidia has been making sure that its performance gains don't come at the expense of flexibility.

"Development of these neural nets is so new and is changing so rapidly … if you lock in a particular type of neural network you have no flexibility to take advantage of these innovations," Shapiro said.

Intel's approach

Nvidia's archrival Intel takes an approach closer to Tesla's, co-developing processors that are optimized for integrated software applications.

"We do software-hardware co-design," says Jack Weast, Intel's chief systems architect of autonomous driving solutions. "We let the needs of the software algorithm drive what goes into the hardware. You can do a much, much more efficient implementation of portions of an algorithm if you know what that algorithm is in advance."

Intel's recent acquisition of the Israeli automotive computer vision company Mobileye, whose EyeQ3 chip powered Tesla's first generation of Autopilot hardware, gives Intel a significant head start, Weast says.

"Unlike some companies who are delivering their first deep-learning accelerator chip to market, we're actually on our third generation," he says. The latest chip, called EyeQ5, will start appearing in cars on the road in 2019. A recent Reuters report said the chip will be in as many as 8 million automated vehicles starting in 2021.

Intel and Nvidia's different approaches highlight how divergent autonomous vehicle development strategies can be, with some automakers seeking an efficiently optimized hardware-software package like Intel's, and others preferring the raw power and flexibility of Nvidia's chips and boards.

The history of Tesla's relationships with both companies suggests that it bridles at both, having publicly complained about the limitations of both Mobileye's relatively mature products as well as the relative inefficiency of Nvidia's.

Tesla's preferences

Perhaps the biggest question about Tesla's move toward more specialized silicon is whether it has really reached a point of software maturity where it makes sense to start optimizing its hardware. And even if it has, there are questions about its ability to keep pace with the powerhouse firms that dedicate massive r&d budgets to continuously improving their offerings.

"Tesla is not a giant chip company," Ramsey says. "Nvidia is spending billions of dollars investing in this technology, mostly subsidized by its incredibly healthy video game business. Intel, similarly, can pour massive resources into the design and validation of the chips. They both either own or have good relationships with huge chip manufacturers. Tesla is unlikely to save money and could produce a product that doesn't perform as well in the field."

Some scrappier startups are rethinking the way chips are placed in the vehicle, putting deep-learning chips near the sensors rather than near the centralized stack. Orr Danon, founder and CEO of one such company called Hailo Technologies, sees great opportunities for "fresh thinking about how we imagine a computer operating" in the autonomous vehicles of the future. But, he warns, there are challenges of trying to prematurely sell rapidly changing cutting-edge technologies.

"This is an exciting and essential step, but we all have to be aware that the road ahead to a stable technology is long, and do our best to understand how to make the overall path as smooth as possible," he says.

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Autonomous car boosters want to reprogram pedestrians

A self-driving Chevrolet Bolt in tests on the streets of San Francisco. Photo credit: REUTERS

You’re crossing the street wrong.

That is essentially the argument some self-driving car boosters have fallen back on in the months after the first pedestrian death attributed to an autonomous vehicle and amid growing concerns that artificial intelligence capable of real-world driving is further away than many predicted just a few years ago.

In a line reminiscent of Steve Jobs’s famous defense of the iPhone 4’s flawed antennae -- “Don't hold it like that” -- these technologists say the problem isn’t that self-driving cars don’t work, it’s that people act unpredictably.

“What we tell people is, ‘Please be lawful and please be considerate,’” says Andrew Ng, a well-known machine learning researcher who runs a venture fund that invests in AI-enabled companies, including self-driving startup Drive.AI. In other words: no jaywalking.

Whether self-driving cars can correctly identify and avoid pedestrians crossing streets has become a burning issue since March after an Uber self-driving car killed a woman in Arizona who was walking a bicycle across the street at night outside a designated crosswalk. The incident is still under investigation, but a preliminary report from federal safety regulators said the car’s sensors had detected the woman but its decision-making software discounted the sensor data, concluding it was likely a false positive.

Google affiliate Waymo has promised to launch a self-driving taxi service, starting in Phoenix, Arizona, later this year, and General Motors has pledged a rival service -- using a car without steering wheel or pedals -- some time in 2019. But it’s unclear if either will be capable of operating outside of designated areas or without a safety driver who can take over in an emergency.

Meanwhile, other initiatives are losing steam.

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Elon Musk has shelved plans for an autonomous Tesla to drive across the U.S. Uber has axed a self-driving truck program to focus on autonomous cars. Daimler Trucks, part of Daimler AG, now says commercial driverless trucks will take at least five years. Others, including Musk, had previously predicted such vehicles would be road-ready by 2020.

With these timelines slipping, driverless proponents like Ng say there’s one surefire shortcut to getting self-driving cars on the streets sooner: persuade pedestrians to behave less erratically. If they use crosswalks, where there are contextual clues -- pavement markings and stop lights -- the software is more likely to identify them.

But to others the very fact that Ng is suggesting such a thing is a sign that today’s technology simply can’t deliver self-driving cars as originally envisioned.

“The AI we would really need hasn't yet arrived,” says Gary Marcus, a New York University professor of psychology who researches both human and artificial intelligence. He says Ng is “just redefining the goalposts to make the job easier,” and that if the only way we can achieve safe self-driving cars is to completely segregate them from human drivers and pedestrians, we already had such technology: trains.

'What just happened?'

Rodney Brooks, a well-known robotics researcher and an emeritus professor at the Massachusetts Institute of Technology, wrote in a blog post critical of Ng’s sentiments that “the great promise of self-driving cars has been that they will eliminate traffic deaths. Now [Ng] is saying that they will eliminate traffic deaths as long as all humans are trained to change their behavior? What just happened?”

Ng argues that humans have always modified their behavior in response to new technology, especially modes of transportation. “If you look at the emergence of railroads, for the most part people have learned not to stand in front of a train on the tracks,” he says. Ng also notes that people have learned that school buses are likely to make frequent stops and that when they do, small children may dart across the road in front of the bus, and so they drive more cautiously. Self-driving cars, he says, are no different.

In fact, jaywalking only became a crime in most of the U.S. because automobile manufacturers lobbied intensively for it in the early 1920s, in large measure to head off strict speed limits and other regulation that might have impacted car sales, according to Peter Norton, a history professor at the University of Virginia who wrote a book on the topic. So there is a precedent for regulating pedestrian behavior to make way for new technology.

And while Ng may be the most prominent self-driving proponent calling for training humans, as well as vehicles, he’s not alone. “There should be proper education programs to make people familiar with these vehicles, the ways to interact with them and to use them,” says Shuchisnigdha Deb, a researcher at Missippi State University’s Center for Advanced Vehicular Systems. The U.S. Department of Transportation has stressed the need for such consumer education in its latest guidance on autonomous vehicles.

Maya Pindeus, the co-founder and CEO of Humanising Autonomy, a London startup working on models of pedestrian behavior and gestures that self-driving car companies can use, likens such lessons to public awareness campaigns Germany and Austria instituted in the 1960s following a spate of jaywalking fatalities. Such efforts helped reduce pedestrian road fatalities in Germany from more than 6,000 deaths in 1970 to less than 500 in 2016, the last year for which figures are available.

The industry is understandably keen not to be seen offloading the burden onto pedestrians. Uber and Waymo both said in emailed statement that their goal is to develop self-driving cars that can handle the world as it is, without being dependent on changing human behavior.

One challenge for these and other companies is that driverless cars are such a novelty right now, pedestrians don’t always act the way they do around regular vehicles. Some people just can’t suppress the urge to test the technology’s artificial reflexes. Waymo, which is owned by Alphabet Inc., routinely encounters pedestrians who deliberately try to “prank” its cars, continually stepping in front of them, moving away and then stepping back in front of them, to impede their progress.

The assumption seems to be that driverless cars are designed to be extra cautious so the practical joke is worth the risk. “Although our systems do have super-human perception, sometimes people seem to think Newton’s laws no longer apply,” says Paul Newman, the co-founder of Oxbotica, a U.K. startup making autonomous driving software, who recalls the time a pedestrian ran up behind a self-driving car and jumped suddenly in front of it.

Over time driverless cars will become less fascinating, and people will presumably be less likely to prank them. In the meantime, the industry is debating what step companies should take to make humans aware of the cars and their intentions.

Dayglo orange

Drive.AI, which was co-founded by Ng’s wife, Carole Riley, has made a number of modifications to the self-driving cars it’s road testing in Frisco, Texas. They’re painted a distinctive dayglo orange, increasing the chance that people will notice them and recognize them as self-driving. Drive.AI also pioneered the use of an external LED-display screen, similar to the ones many city buses use to display their destination or route number, that can convey the car's intentions to humans. For instance, a car stopped at a crosswalk, might display the message: “Waiting for you to cross.”

Uber has taken this idea further, filing patents for a system that would include a variety of flashing external signage and holograms projected in front of the car to communicate with human drivers and pedestrians. Google has also filed patents for its own external signage. Oxbotica’s Newman says he likes the idea of such external messaging as well as distinctive sounds -- much like the beeping noise large vehicles make when reversing-- to help ensure safe interactions between humans and autonomous vehicles.

Deb says her research shows that people want external features and audible communication or warning sounds of some kind. But so far, besides Drive.AI, the cars these companies are using in road tests don’t include such modifications. It’s also not clear how pedestrians or other human drivers could communicate their intentions to self-driving vehicles, something Deb says may also be necessary to avoid accidents in the future.

Pindeus’s company wants those building self-driving cars to focus more on understanding the non-verbal cues and hand gestures people use to communicate. The problem with most of the computer vision systems that self-driving cars use, she says, is they simply put a boundary box around an object and apply a label -- parked car, bicycle, person -- without the ability to analyze anything happening inside that box.

Eventually, better computer vision systems and better AI may solve this problem. Over time, cities will probably remake themselves for an autonomous age with “geofencing” -- a fancy term for creating separate zones and designated pickup spots for self-driving cars and taxis.

In the meantime, your parents’ advice probably still applies: Don’t jaywalk and look both ways before crossing the street.

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