Uber Traded Away Its In-House Self-Driving Effort

Perhaps the best-known ridesharing service, Uber has grown rapidly over the last decade. Since its founding in 2009, it has expanded into markets around the globe, and entered the world of food delivery and even helicopter transport.

One of the main headline research areas for the company was the development of autonomous cars, which would revolutionize the company’s business model by eliminating the need to pay human drivers. However, as of December, the company has announced that it it spinning off its driverless car division in a deal reportedly worth $4 billion, though that’s all on paper — Uber is trading its autonomous driving division, and a promise to invest a further $400 million, in return for a 26% share in the self-driving tech company Aurora Innovation.

Playing A Long Game

Uber’s self-driving efforts have been undertaken in close partnership with Volvo in recent years.

Uber’s driverless car research was handled by the internal Advanced Technologies Group, made up of 1,200 employees dedicated to working on the new technology. The push to eliminate human drivers from the ride-sharing business model was a major consideration for investors of Uber’s Initial Public Offering on the NYSE in 2019. The company is yet to post a profit, and reducing the amount of fares going to human drivers would make it much easier for the company to achieve that crucial goal.

However, Uber’s efforts have not been without incident. Tragically, in 2018, a development vehicle running in autonomous mode hit and killed a pedestrian in Tempe, Arizona. This marked the first pedestrian fatality caused by an autonomous car, and led to the suspension of on-road testing by the company. The incident revealed shortcomings in the company’s technology and processes, and was a black mark on the company moving forward.

The Advanced Technology Group (ATG) has been purchased by a Mountain View startup by the name of Aurora Innovation, Inc. The company counts several self-driving luminaries amongst its cofounders. Chris Urmson, now CEO, was a technical leader during his time at Google’s self-driving research group. Drew Bagnell worked on autonomous driving at Uber, and Sterling Anderson came to the startup from Tesla’s Autopilot program. The company was founded in 2017, and counts Hyundai and Amazon among its venture capital investors.

Aurora could also have links with Toyota, which also invested in ATG under Uber’s ownership in 2019. Unlike Uber, which solely focused on building viable robotaxis for use in limited geographical locations, the Aurora Driver, the core of the company’s technology, aims to be adaptable to everything from “passenger sedans to class-8 trucks”.

Aurora has been developing self-driving technology to handle real-world situations since its founding in 2017. Being able to master the challenges of a crowded city will be key to succeeding in the marketplace.

Getting rid of ATG certainly spells the end of Uber’s in-house autonomous driving effort, but it doesn’t mean they’re getting out of the game. Holding a stake in Aurora, Uber still stands to profit from early investment, and will retain access to the technology as it develops. At the same time, trading ATG off to an outside firm puts daylight between the rideshare company and any negative press from future testing incidents.

Even if Aurora only retains 75% of ATG’s 1,200 employees, it’s doubling in size, and will be worth keeping an eye on in the future.

Remoticon Video: Learn How To Hack A Car With Amith Reddy

There was a time not too long ago when hacking a car more often than not involved literal hacking. Sheet metal was cut, engine cylinders were bored, and crankshafts were machined to increase piston travel. It was all in the pursuit of milking the last ounce performance out of every drop of gasoline, along with a little personal expression in the form of paint and chrome.

While it’s still possible — and encouraged — to hack cars thus, the inclusion of engine control units and other systems to our rides has created an entirely different universe of car hacking options, which Amith Reddy distilled into his very popular workshop at the 2020 Remoticon. The secret sauce behind all the hacks you can accomplish in today’s drive-by-wire cars is the Controller Area Network (CAN), the network used to connect the array of sensors, actuators, and controllers that lie under the metal and plastic of modern cars.

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Alfred Jones Talks About The Challenges Of Designing Fully Self-Driving Vehicles

The leap to self-driving cars could be as game-changing as the one from horse power to engine power. If cars prove able to drive themselves better than humans do, the safety gains could be enormous: auto accidents were the #8 cause of death worldwide in 2016. And who doesn’t want to turn travel time into something either truly restful or alternatively productive?

But getting there is a big challenge, as Alfred Jones knows all too well. The Head of Mechanical Engineering at Lyft’s level-5 self-driving division, his team is building the roof racks and other gear that gives the vehicles their sensors and computational hardware. In his keynote talk at Hackaday Remoticon, Alfred Jones walks us through what each level of self-driving means, how the problem is being approached, and where the sticking points are found between what’s being tested now and a truly steering-wheel-free future.

Check out the video below, and take a deeper dive into the details of his talk.

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Open Source Self-Driving Smartphone Robot

Our smartphones are incredibly powerful computers in their own right, yet we don’t often see them directly integrated into projects. Intel Intelligent Systems Lab has done exactly that with the release OpenBot, an open source smartphone based self-driving robot.

Most of the magic happens on the smartphone, which runs an app built on TensorFlow Lite, and integrates the camera and array of sensors on the smartphone, as well as the data from ultrasonic sensors and wheel encoders on the robot. The robot itself is relatively simple, with four geared DC motors, motor drivers wired to an Arduino Nano that interfaces with an Android Phone over serial.

The app created by the Intel ISL team comes preloaded with three AI models that can do either person following, or two different modes of autonomous navigation. By connecting a Bluetooth controller to the smartphone and drive the robot around manually in your specific environment while collecting data, you can train a custom autonomous driving policy to suit your environment.

This looks like an excellent way to get a taste of autonomous robots on a small budget, while still being a viable base for more demanding applications. We’ve seen only a few smartphone based robots like DriveMyPhone and SmartiPresense, which don’t have AI capabilities, but are intended for telepresence applications. We’ve always wondered why we don’t see more projects with cellphones, so we welcome the example.

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Self-Driving RC Truck Is A Master’s Thesis In Cybernetics And Robotics

RC cars are a fun pastime, but for many hackers, taking things to the next level involves making the cars drive themselves. For his Masters thesis, [Jon] did just that, building a self-driving robot truck that confidently cruises the floor of his laboratory.

The truck is based on a 1/14th scale Tamiya chassis, and had been fitted out by a prior group with an inductive charging system. On top of this platform, [Jon] added a Jetson TX2 to act as the brains of the system, hooking it up with a Slamtec RPLIDAR scanner to map its surrounding environment. There’s also a Teensy microcontroller onboard which handles synthesizing PWM signals for the radio control hardware that drives the truck, and a Logitech webcam up front for machine vision. The truck is capable of operating in a variety of modes, from full manual operation, to driving based on LIDAR mapping or with an AI controlling the truck based on camera data. The truck is programmed to drive a route including an inductive charging pad so it can keep its power levels up without human intervention.

It’s a great blueprint for a self-driving system, and [Jon]’s thesis goes into great detail on how everything works at the base level (available on this page as a 67 MB PDF). His Code is on Github for the curious. We’ve seen similar projects before too, like this robot that navigates its builder’s house using LIDAR. Video after the break.

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Alfred Jones And Kipp Bradford To Deliver Keynotes At Remoticon Next Week

There’s just one week left until Hackaday Remoticon, our online gathering in place of our traditional in-person conference during this time of social distancing. Joining the more than 20 hands-on workshops that make up the bulk of Remoticon, we’re excited to announce the two keynote speakers who will be taking the virtual stage: Alfred Jones and Kipp Bradford.

Tickets to see these keynote talks, to watch the SMD Challenge, to see hardware demos, and to take part in the show and tell are free, so get yours today!

 

Alfred Jones

Alfred Jones

Head of Mechanical Engineering at Lyft’s Self-Driving Division

Alfred Jones is the Head of Mechanical Engineering at Lyft’s level 5 self-driving division. Level 5 means there are no humans involved in operating the vehicle and it is still capable of driving anywhere a human could have. What goes into modifying a vehicle for this level of self-driving? What processes does his team use to deliver safe automation? And will cars in the near future completely get rid of the driver’s seat? Alfred knows and we’ll be hanging on his every word!

Kipp BradfordKipp Bradford

CTO fo Treau

Kipp Bradford is the CTO of Treau, a company bringing heating, ventilation, and air conditioning (HVAC) into the information age. These systems contribute as much as 20% of global emissions each year, so even small efficiency gains stand to have a huge impact. The industry has remained nearly unchanged for decades, and Kipp is at the forefront of evolving the hidden systems found in nearly every building. Will the air conditioner of tomorrow make the one we have today look like a rotary telephone? We look forward to hearing what Kipp has to say about it.

We’re so excited to have these two phenomenal speakers who have also both been involved as expert judges in the Hackaday Prize (Alfred in 2020, Kipp in 2017 and 2018). Help us show our appreciation by packing the virtual lecture halls for their talks on Saturday, November 7th! Get your free ticket now.

Garbage Can Takes Itself Out

Home automation is a fine goal but typically remains confined to lights, blinds, and other things that are relatively stationary and/or electrical in nature. There is a challenge there to be certain, but to really step up your home automation game you’ll need to think outside the box. This automated garbage can that can take itself out, for example, has all the home automation street cred you’d ever need.

The garbage can moves itself by means of a scooter wheel which has a hub motor inside and is powered by a lithium battery, but the real genius of this project is the electronics controlling everything. A Raspberry Pi Zero W is at the center of the build which controls the motor via a driver board and also receives instructions on when to wheel the garbage can out to the curb from an Nvidia Jetson board. That board is needed because the creator, [Ahad Cove], didn’t want to be bothered to tell his garbage can to take itself out or even schedule it. He instead used machine learning to detect when the garbage truck was headed down the street and instruct the garbage can to roll itself out then.

The only other thing to tie this build together was to get the garage door to open automatically for the garbage can. Luckily, [Ahad]’s garage door opener was already equipped with WiFi and had an available app, unbeknownst to him, which made this a surprisingly easy part of the build. If you have a more rudimentary garage door opener, though, there are plenty of options available to get it on the internets.

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