Tag Archives: Transportation/Self-Driving

Driving Tests Coming for Autonomous Cars

Post Syndicated from Jeff Hecht original https://spectrum.ieee.org/cars-that-think/transportation/self-driving/driving-tests-coming-for-autonomous-cars

The three laws of robotic safety in Isaac Asimov’s science fiction stories seem simple and straightforward, but the ways the fictional tales play out reveal unexpected complexities. Writers of safety standards for self-driving cars express their goals in similarly simple terms. But several groups now developing standards for how autonomous vehicles will interact with humans and with each other face real-world issues much more complex than science fiction.

Advocates of autonomous cars claim that turning the wheel over to robots could slash the horrific toll of 1.3 million people killed around the world each year by motor vehicles. Yet the public has become wary because robotic cars also can kill. Documents released last week by the U.S. National Transportation Safety Board blame the March 2018 death of an Arizona pedestrian struck by a self-driving Uber on safety failures by the car’s safety driver, the company, and the state of Arizona. Even less-deadly safety failures are damning, like the incident where a Tesla in Autopilot mode wasn’t smart enough to avoid crashing into a stopped fire engine whose warning lights were flashing.

Safety standards for autonomous vehicles “are absolutely critical” for public acceptance of the new technology, says Greg McGuire, associate director of the Mcity autonomous vehicle testing lab at the University of Michigan. “Without them, how do we know that [self-driving cars] are safe, and how do we gain public trust?” Earning that trust requires developing standards through an open process that the public can scrutinize, and may even require government regulation, he adds.

Companies developing autonomous technology have taken notice. Earlier this year, representatives from 11 companies including Aptiv, Audi, Baidu, BMW, Daimler, Infineon, Intel, and Volkswagen collaborated to write a wide-ranging whitepaper titled “Safety First for Automated Driving.” They urged designing safety features into the automated driving function, and using heightened cybersecurity to assure the integrity of vital data including the locations, movement, and identification of other objects in the vehicle environment. They also urged validating and verifying the performance of robotic functions in a wide range of operating conditions.

On 7 November, the International Telecommunications Union announced the formation of a focus group called AI for Autonomous and Assisted Driving. It’s aim: to develop performance standards for artificial intelligence (AI) systems that control self-driving cars. (The ITU has come a long way since its 1865 founding as the International Telegraph Union, with a mandate to standardize the operations of telegraph services.)

ITU intends the standards to be “an equivalent of a Turing Test for AI on our roads,” says focus group chairman Bryn Balcombe of the Autonomous Drivers Alliance. A computer passes a Turing Test if it can fool a person into thinking it’s a human. The AI test is vital, he says, to assure that human drivers and the AI behind self-driving cars understand each other and predict each other’s behaviors and risks.

A planning document says AI development should match public expectations so:

• AI never engages in careless, dangerous, or reckless driving behavior

• AI remains aware, willing, and able to avoid collisions at all times

• AI meets or exceeds the performance of a competent, careful human driver


These broad goals for automotive AI algorithms resemble Asimov’s laws, insofar as they bar hurting humans and demand that they obey human commands and protect their own existence. But the ITU document includes a list of 15 “deliverables” including developing specifications for evaluating AIs and drafting technical reports needed for validating AI performance on the road.

A central issue is convincing the public to entrust the privilege of driving—a potentially life-and-death activity—to a technology which has suffered embarrassing failures like the misidentification of minorities that led San Francisco to ban the use of facial recognition by police and city agencies.

Testing how well an AI can drive is vastly complex, says McGuire. Human adaptability makes us fairly good drivers. “We’re not perfect, but we are very good at it, with typically a hundred million miles between fatal traffic crashes,” he says. Racking up that much distance in real-world testing is impractical—and it is but a fraction of the billions of vehicle miles needed for statistical significance. That’s a big reason developers have turned to simulations. Computers can help them run up virtual mileage needed to find potential safety flaws that might arise only rare situations, like in a snowstorm or heavy rain, or on a road under construction.

It’s not enough for an automotive AI to assure the vehicle’s safety, says McGuire. “The vehicle has to work in a way that humans would understand.” Self-driving cars have been rear-ended when they stopped in situations where most humans would not have expected a driver to stop. And a truck can be perfectly safe even when close enough to unnerve a bicyclist.

Other groups are also developing standards for robotic vehicles. ITU is covering both automated driver assistance and fully autonomous vehicles. Underwriters Laboratories is working on a standard for fully-autonomous vehicles. The Automated Vehicle Safety Consortium, a group including auto companies, plus Lyft, Uber, and SAE International (formerly the Society of Automotive Engineers) is developing safety principles for SAE Level 4 and 5 autonomous vehicles. The BSI Group (formerly the British Institute of Standards) developed a strategy for British standards for connected and autonomous vehicles and is now working on the standards themselves.

How long will it take to develop standards? “This is a research process,” says McGuire. “It takes as long as it takes” to establish public trust and social benefit. In the near term, Mcity has teamed with the city of Detroit, the U.S. Department of Transportation, and Verizon to test autonomous vehicles for transporting the elderly on city streets. But he says the field “needs to be a living thing that continues to evolve” over a longer period.

NTSB Investigation Into Deadly Uber Self-Driving Car Crash Reveals Lax Attitude Toward Safety

Post Syndicated from Mark Harris original https://spectrum.ieee.org/cars-that-think/transportation/self-driving/ntsb-investigation-into-deadly-uber-selfdriving-car-crash-reveals-lax-attitude-toward-safety

The Uber car that hit and killed Elaine Herzberg in Tempe, Ariz., in March 2018 could not recognize all pedestrians, and was being driven by an operator likely distracted by streaming video, according to documents released by the U.S. National Transportation Safety Board (NTSB) this week.

But while the technical failures and omissions in Uber’s self-driving car program are shocking, the NTSB investigation also highlights safety failures that include the vehicle operator’s lapses, lax corporate governance of the project, and limited public oversight.

This week, the NTSB released over 400 pages ahead of a 19 November meeting aimed at determining the official cause of the accident and reporting on its conclusions. The Board’s technical review of Uber’s autonomous vehicle technology reveals a cascade of poor design decisions that led to the car being unable to properly process and respond to Herzberg’s presence as she crossed the roadway with her bicycle.

A radar on the modified Volvo XC90 SUV first detected Herzberg roughly six seconds before the impact, followed quickly by the car’s laser-ranging lidar. However, the car’s self-driving system did not have the capability to classify an object as a pedestrian unless they were near a crosswalk.

For the next five seconds, the system alternated between classifying Herzberg as a vehicle, a bike and an unknown object. Each inaccurate classification had dangerous consequences. When the car thought Herzberg a vehicle or bicycle, it assumed she would be travelling in the same direction as the Uber vehicle but in the neighboring lane. When it classified her as an unknown object, it assumed she was static.

Worse still, each time the classification flipped, the car treated her as a brand new object. That meant it could not track her previous trajectory and calculate that a collision was likely, and thus did not even slow down. Tragically, Volvo’s own City Safety automatic braking system had been disabled because its radars could have interfered with Uber’s self-driving sensors.

By the time the XC90 was just a second away from Herzberg, the car finally realized that whatever was in front of it could not be avoided. At this point, it could have still slammed on the brakes to mitigate the impact. Instead, a system called “action suppression” kicked in.

This was a feature Uber engineers had implemented to avoid unnecessary extreme maneuvers in response to false alarms. It suppressed any planned braking for a full second, while simultaneously alerting and handing control back to its human safety driver. But it was too late. The driver began braking after the car had already hit Herzberg. She was thrown 23 meters (75 feet) by the impact and died of her injuries at the scene.

Four days after the crash, at the same time of night, Tempe police carried out a rather macabre re-enactment. While an officer dressed as Herzberg stood with a bicycle at the spot she was killed, another drove the actual crash vehicle slowly towards her. The driver was able to see the officer from at least 194 meters (638 feet) away.

Key duties for Uber’s 254 human safety drivers in Tempe were actively monitoring the self-driving technology and the road ahead. In fact, recordings from cameras in the crash vehicle show that the driver spent much of the ill-fated trip looking at something placed near the vehicle’s center console, and occasionally yawning or singing. The cameras show that she was looking away from the road for at least five seconds directly before the collision.

Police investigators later established that the driver had likely been streaming a television show on her personal smartphone. Prosecutors are reportedly still considering criminal charges against her.

Uber’s Tempe facility, nicknamed “Ghost Town,” did have strict prohibitions against using drugs, alcohol or mobile devices while driving. The company also had a policy of spot-checking logs and in-dash camera footage on a random basis. However, Uber was unable to supply NTSB investigators with documents or logs that revealed if and when phone checks were performed. The company also admitted that it had never carried out any drug checks.

Originally, the company had required two safety drivers in its cars at all times, with operators encouraged to report colleagues who violated its safety rules. In October 2017, it switched to having just one.

The investigation also revealed that Uber didn’t have a comprehensive policy on vigilance and fatigue. In fact, the NTSB found that Uber’s self-driving car division “did not have a standalone operational safety division or safety manager. Additionally, [it] did not have a formal safety plan, a standardized operations procedure (SOP) or guiding document for safety.”

Instead, engineers and drivers were encouraged to follow Uber’s core values or norms, which include phrases such as: “We have a bias for action and accountability”; “We look for the toughest challenges, and we push”; and, “Sometimes we fail, but failure makes us smarter.”

NTSB investigators found that state of Arizona had a similarly relaxed attitude to safety. A 2015 executive order from governor Doug Ducey established a Self-Driving Vehicle Oversight Committee. That committee met only twice, with one of its representatives telling NTSB investigators that “the committee decided that many of the [laws enacted in other states] stifled innovation and did not substantially increase safety. Further, it felt that as long as the companies were abiding by the executive order and existing statutes, further actions were unnecessary.”

When investigators inquired whether the committee, the Arizona Department of Transportation, or the Arizona Department of Public Safety had sought any information from autonomous driving companies to monitor the safety of their operations, they were told that none had been collected.

As it turns out, the fatal collision was far from the first crash that Uber’s 40 self-driving cars in Tempe had been involved in. Between September 2016 and March 2018, the NTSB learned there had been 37 other crashes and incidents involving Uber’s test vehicles in autonomous mode. Most were minor rear-end fender-benders, but on one occasion, a test vehicle drove into a bicycle lane bollard. Another time, a safety driver had been forced to take control of the car to avoid a head-on collision. The result: the car struck a parked vehicle.

Virtual Car Sharing Combines Telepresence Robots and Autonomous Vehicles

Post Syndicated from Evan Ackerman original https://spectrum.ieee.org/cars-that-think/transportation/self-driving/virtual-car-sharing-combines-telepresence-robots-and-autonomous-vehicles

5G connectivity and a remotely projected human help autonomous vehicles be safer and more flexible

One of the remaining challenges for autonomous cars is figuring out a way to handle that long tail of weird edge cases that can randomly happen in the real world. Another challenge that remains is figuring out how to handle that huge population of weird beings called humans, who behave pseudorandomly in the real world. 

We’ve seen potential solutions to both of these problems. The first, meant to cover the long tail of situations that are outside the experience and confidence of an autonomous system, can involve a remote human temporarily taking over control of the vehicle. And with the right hardware, that human can solve the challenge of interacting with other humans at the same time.

One Driver Steers Two Trucks With Peloton’s Autonomous Follow System

Post Syndicated from Tekla S. Perry original https://spectrum.ieee.org/view-from-the-valley/transportation/self-driving/will-autonomous-following-be-a-game-changer-for-trucking

The technology is currently being tested on closed tracks, the company says

A host of companies are working to develop autonomous driving technology, but Silicon Valley startup Peloton has put its focus on autonomous following. The company today announced technology that uses computers, sensors, and vehicle-to-vehicle (V2V) communications to allow one driver to drive two separate trucks. 

Last year, Peloton began selling technology that enabled closer and safer truck platooning, using sensors, V2V communications, and automatic powertrain control and braking. That version of its product, Platoon Pro, requires a driver in the second truck to steer. The new version will take the second driver out of the equation.

Here’s how it works: In the front truck, the driver drives normally. Whenever he adjusts his foot on the throttle, touches the brakes, or maneuvers the steering wheel, digital details describing that action are wirelessly transmitted to the computer in the following truck. Using that information, along with data gathered from its own collection of radars, cameras, and other sensors, the second truck can safely trail close behind the first, forming a single-driver platoon.

The Self-Driving Car Is a Surveillance Tool

Post Syndicated from Mark Anderson original https://spectrum.ieee.org/cars-that-think/transportation/self-driving/surveillance-and-the-selfdriving-car

In the coming age of autonomous vehicles, users may have to pay extra to keep their whereabouts private

Most drivers today can still remember when GPS was provided by a portable device plugged into the car’s cigarette lighter and mounted on the windshield with a suction cup. But soon after the iPhone arrived, GPS (or Sat Nav for U.K. readers) became just another app.

Now, an American geography researcher is arguing that GPS’s transition from dedicated hardware to smartphone software was even more significant than we realize. He says mobile mapping apps also foreshadow the ultimate transformation of car companies from purely “hardware” manufacturers to hybrid hardware, software, and service providers.

With that tectonic shift, he says, will come another shift toward a transportation economy in which the prime commodity is not just the car, but also the driver (his example echoes a larger trend which the sociologist Shoshana Zuboff calls “Surveillance Capitalism”).

“What we have with smartphones is, now [GPS] data can be monetized in other ways,” says Luis Alvarez León, assistant professor of geography at Dartmouth College. “Information companies are providing the mapping service as an ancillary way of refining their search algorithms, of collecting more data about the consumers… [and] of repackaging it for other third parties.”

Ultrafast Motion-Planning Chip Could Make Autonomous Cars Safer

Post Syndicated from Evan Ackerman original https://spectrum.ieee.org/cars-that-think/transportation/self-driving/realtime-robotics-motion-planning-chip-autonomous-cars

Realtime Robotics’ motion-planning processor helps autonomous cars make better decisions

About two years ago, we covered a research project from Duke University that sped up motion planning for a tabletop robot arm by several orders of magnitude. The robot relied on a custom processor to do in milliseconds what normally takes seconds. The Duke researchers formed a company based on this tech called Realtime Robotics, and recently they’ve been focused on applying it to autonomous vehicles.

The reason that you should care about fast motion planning for autonomous vehicles is because motion planning encompasses the process by which the vehicle decides what it’s going to do next. Making this process faster doesn’t just mean that the vehicle can make decisions more quickly, but that it can make much better decisions as well—keeping you, and everyone around you, as safe as possible.