An Android Phone Powers A Self Driving Car

As auto manufacturers have brought self-driving features to their products, we’re told about how impressive their technologies are and just how much computing power is on board to make it happen. Thus it surprised us (and it might also surprise you too) that some level of self-driving can be performed by an Android phone. [Mankaran Singh] has the full details.

It starts with the realization that a modern smartphone contains the necessary sensors to  perform basic self-driving, and then moves on to making a version of openpilot that can run on more than the few supported phones. It’s not the driver-less car of science fiction but one which performs what we think is SAE level 2 self driving, which is cruise control, lane centering, and collision warning. They take it out on the road in a little Suzuki on a busy Indian dual carriageway in the rain, and while we perhaps place more trust in meat-based driving, it does seem to perform fairly well

Self driving features are codified into a set of levels for an easy reference on what each is capable of doing. We’ve taken a look at it in the past, should you be interested.

Ask Hackaday: Why Do Self Driving Cars Keep Causing Traffic Jams?

Despite what some people might tell you, self-driving cars aren’t really on the market yet. Instead, there’s a small handful of startups and big tech companies that are rapidly developing prototypes of this technology. These vehicles are furiously testing in various cities around the world.

In fact, depending on where you live, you might have noticed them out and about. Not least because many of them keep causing traffic jams, much to the frustration of their fellow road users. Let’s dive in and look at what’s going wrong.

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Forgotten Tech — Self Driving Cars

The notion of self driving cars isn’t new. You might be surprised at the number of such projects dating back to the 1920s. Many of these systems relied on external aids built into the roadways. It’s only recently that self driving cars on existing roadways are becoming closer to reality than fiction — increased computer processing power, smaller and power-efficient computers, compact Lidar and millimeter-wave Radar sensors are but a few enabling technologies. In South Korea, [Prof Min-hong Han] and his team of students took advantage of these technological advances and built an autonomous car which successfully navigated the streets of Seoul in several field trials. A second version subsequently drove itself along the 300 km journey from Seoul to the southern port city Busan. You might think this is boring news, until you realize this was accomplished back in the early 1990s using an Intel 386-powered desktop computer.

The project created a lot of buzz at the time, and was shown at the Daejeon Expo ’93 international exposition. Alas, the government eventually decided to cancel the research program, as it didn’t fit into their focus on heavy industries like ship building and steel production. Given the tremendous focus on self-driving and autonomous vehicles today, and with the benefit of hindsight, we wonder if that was the best choice. This isn’t the only decision from Seoul that seems questionable when viewed from the present — Samsung executives famously declined to buy Andy Rubin’s new operating system for digital cameras and handsets back in late 2004, and a few weeks later Android was purchased by Google.

You should check out [Prof Han]’s YouTube channel showing videos of the car’s camera while operating in various conditions and overlaid with the lane recognition markers and other information. I’ve driven the streets of Seoul, and that alone can be a frightening experience. But [Han] manages to stretch out in the back seat, so confident in his system that he doesn’t even wear a seatbelt.

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Tesla Begins “Full Self Driving” Public Beta As Waymo And Cruise Go Unattended

Self-driving technology is a holy grail that promises to forever change the way we interact with cars. Thus far, there’s been plenty of hype and excitement, but full vehicles that remove the driver from the equation have remained far off. Tesla have long posited themselves as a market leader in this area, with their Autopilot technology allowing some limited autonomy on select highways. However, in a recent announcement, they have heralded the arrival of a new “Full Self Driving” ability for select beta testers in their early access program.

Taking Things Up A Notch

Telsa’s update notes highlight the new “Full Self-Driving” capabilities. Drivers are expected to pay continuous attention and be prepared to take over at any time, as the system “may do the wrong thing at the worst time.”

The new software update further extends the capabilities of Tesla vehicles to drive semi-autonomously. Despite the boastful “Full Self Driving” moniker, or FSD for short, it’s still classified as a Level 2 driving automation system, which relies on human intervention as a backup. This means that the driver must be paying attention and ready to take over in an instant, at all times. Users are instructed to keep their hands on the wheel at all times, but predictably, videos have already surfaced of users ignoring this measure.

The major difference between FSD and the previous Autopilot software is the ability to navigate city streets. Formerly, Tesla vehicles were only able to self-drive on highways, where the more regular flow of traffic is easier to handle. City streets introduce far greater complexity, with hazards like parked cars, pedestrians, bicycles, and complicated intersections. Unlike others in the field, who are investing heavily in LIDAR technology, Tesla’s system relies entirely on cameras and radar to navigate the world around it. Continue reading “Tesla Begins “Full Self Driving” Public Beta As Waymo And Cruise Go Unattended”

Was The Self Driving Car Invented In The 1980s?

The news is full of self-driving cars and while there is some bad news, most of it is pretty positive. It seems a foregone conclusion that it is just a matter of time before calling for an Uber doesn’t involve another person. But according to a recent article, [Ernst Dickmanns] — a German aerospace engineer —  built three autonomous vehicles starting in 1986 and culminating with on-the-road demonstrations in 1994 for Daimler.

It is hard to imagine what had to take place to get a self-driving car in 1986. The article asserts that you need computer analysis of video at 10 frames a second minimum. In the 1980s doing a single frame in 10 minutes was considered an accomplishment. [Dickmanns’] vehicles borrowed tricks from how humans drive. They focused on a small area at any one moment and tried to ignore things that were not relevant.

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Table-Top Self Driving With The Pi Zero

Self-driving technologies are a hot button topic right now, as major companies scramble to be the first to market with more capable autonomous vehicles. There’s a high barrier to entry at the top of the game, but that doesn’t mean you can’t tinker at home. [Richard Crowder] has been building a self-driving car at home with the Raspberry Pi Zero.

The self-driving model is trained by first learning from the human driver.

[Richard]’s project is based on the EOgma Neo machine learning library. Using a type of machine learning known as Sparse Predictive Hierarchies, or SPH, the algorithm is first trained with user input. [Richard] trained the model by driving it around a small track. The algorithm takes into account the steering and throttle inputs from the human driver and also monitors the feed from the Raspberry Pi camera. After training the model for a few laps, the car is then ready to drive itself.

Fundamentally, this is working on a much simpler level than a full-sized self-driving car. As the video indicates, the steering angle is predicted based on the grayscale pixel data from the camera feed. The track is very simple and the contrast of the walls to the driving surface makes it easier for the machine learning algorithm to figure out where it should be going. Watching the video feed reminds us of simple line-following robots of years past; this project achieves a similar effect in a completely different way. As it stands, it’s a great learning project on how to work with machine learning systems.

[Richard]’s write-up includes instructions on how to replicate the build, which is great if you’re just starting out with machine learning projects. What’s impressive is that this build achieves what it does with only the horsepower of the minute Raspberry Pi Zero, and putting it all in a package of just 102 grams. We’ve seen similar builds before that rely on much more horsepower – in processing and propulsion.

Self Driving Potato Hits The Road

Potatoes deserve to roam the earth, so [Marek Baczynski] created the first self-driving potato, ushering in a new era of potato rights. Potato batteries have been around forever. Anyone who’s played Portal 2 knows that with a copper and zinc electrode, you can get a bit of current out of a potato. Tubers have been powering clocks for decades in science classrooms around the world. It’s time for something — revolutionary.

[Marek] knew that powering a timepiece wasn’t enough for his potato, so he picked up a Texas Instruments BQ25504 boost converter energy harvesting chip. A potato can output around 0.4 V at 0.6 mA. The 25504 uses this power to slowly charge a capacitor. Every fifteen minutes or so, enough energy is stored to power a motor for a short time. [Marek] built a car for his potato — or more fittingly, he built his potato into a car.

The starch-powered capacitor moves the potato car about 8 cm per cycle. Over the course of a day, the potato can travel around 7.5 meters. Not very far, but hey, that’s further than the average potato travels on its own power. Of course, any traveling potato needs a name, so [Marek] dubbed his new pet “Pontus”. Check out the video after the break to see the ultimate fate of poor Pontus.

Now that potatoes are mobile, we’re going to need a potato detection system. Humanity’s only hope is to fight fire with fire – break out the potato cannons!

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