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Hackaday Links: September 25, 2022

Looks like there’s trouble out at L2, where the James Webb Space Telescope suffered a mechanical anomaly back in August. The issue, which was just announced this week, involves only one of the six imaging instruments at the heart of the space observatory, known as MIRI, the Mid-Infrared Instrument. MIRI is the instrument on Webb that needs the coldest temperatures to work correctly, down to six Kelvins — we’ve talked about the cryocooler needed to do this in some detail. The problem has to do with unexpectedly high friction during the rotation of a wheel holding different diffraction gratings. These gratings are rotated into the optical path for different measurements, but apparently the motor started drawing excessive current during its move, and was shut down. NASA says that this only affects one of the four observation modes of MIRI, and the rest of the instruments are just fine at this time. So they’ve got some troubleshooting to do before Webb returns to a full program of scientific observations.

There’s an old saying that, “To err is human, but to really screw things up takes a computer.” But in Russia, to really screw things up it takes a computer and a human with a really poor grasp on just how delicately balanced most infrastructure systems are. The story comes from Moscow, where someone allegedly spoofed a massive number of fake orders for taxi rides (story in Russian, Google Translate works pretty well) through the aggregator Yandex.Taxi on the morning of September 1. The taxi drivers all dutifully converged on the designated spot, but instead of finding their fares, they just found a bunch of other taxis milling about and mucking up traffic. Yandex reports it has already added protection against such attacks to its algorithm, so there’s that at least. It’s all fun and games until someone causes a traffic jam.

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AI On The Highway

A couple of announcements caught our attention last week regarding AI-controlled cars. South Korea’s Kakao Mobility and local startup Autonomous A2G launched a limited self-driving taxi service in Sejong City this month, made possible by enabling legislation passed in May. For now, the service is restricted to government employees, and the AI driver will be backed-up by an engineer who is there to monitor the systems and take over in an emergency. The companies plan to expand the fleet and service areas this year, although no details are given.

Another announcement comes from the Ministry of Land, Infrastructure and Transport about the on-going successes of the semi-autonomous truck platooning program. This is a collaboration between the Korean Expressway Corporation, Kookmin University in Seoul, and Hyundai Motors. Previously restricted to a designated test road called the Yeoju Smart Highway, the program is now being tested on public roads at speeds up to 70 kph. This year the program will expand to platoons of 4 trucks running at 90 kph. We’ve always thought that long-haul trucking and freight industries would be an early adaptor AI technologies, and one which AI could offer significant benefits.

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A Million Zombie Taxis By 2020? It’s Not Going To Happen

The tech world has a love for Messianic figures, usually high-profile CEOs of darling companies whose words are hung upon and combed through for hidden meaning, as though they had arrived from above to our venture-capital-backed prophet on tablets of stone. In the past it has been Steve Jobs or Bill Gates, now it seems to be Elon Musk who has received this treatment. Whether his companies are launching a used car into space, shooting things down tubes in the desert, or synchronised-landing used booster rockets, everybody’s talking about him. He’s a showman whose many pronouncements are always soon eclipsed by bigger ones to keep his public on the edge of their seats, and now we’ve been suckered in too, which puts us on the spot, doesn’t it.

Your Johnny Cab is almost here

The latest pearl of Muskology came in a late April presentation: that by 2020 there would be a million Tesla electric self-driving taxis on the road. It involves a little slight-of-hand in assuming that a fleet of existing Teslas will be software upgraded to be autonomous-capable and that some of them will somehow be abandoned by their current owners and end up as taxis, but it’s still a bold claim by any standard.

Here at Hackaday, we want to believe, but we’re not so sure. It’s time to have a little think about it all. It’s the start of May, so 2020 is about 7 months away. December 2020 is about 18 months away, so let’s give Tesla that timescale. 18 months to put a million self-driving taxis on the road. Can the company do it? Let’s find out.

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Distributed Air Quality Monitoring Via Taxi Fleet

When [James] moved to Lima, Peru, he brought his jogging habit with him. His morning jaunts to the coast involve crossing a few busy streets that are often occupied by old, smoke-belching diesel trucks. [James] noticed that his throat would tickle a bit when he got back home. A recent study linking air pollution to dementia risk made him wonder how cities could monitor air quality on a street-by-street basis, rather than relying on a few scattered stations. Lima has a lot of taxis, so why wire them up with sensors and monitor the air quality in real-time?

This taxi data logger’s chief purpose is collect airborne particulate counts and illustrate the pollution level with a Google Maps overlay. [James] used a light-scattering particle sensor and a Raspi 3 to send the data to the cloud via Android Things. Since the Pi only has one native UART, [James] used it for the particle sensor and connected the data-heavy GPS module through an FTDI serial adapter. There’s also a GPS to locate the cab and a temperature/humidity/pressure sensor to get a fuller environmental picture.

Take a ride past the break to go on the walk through, and stick around for the testing video if you want to drive around Lima for a bit. Interested in monitoring your own personal air quality? Here’s a DIY version that uses a dust sensor.

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