Fans Add Reality To Virtual Driving

A few decades ago you might have been satisfied with a crude wireframe flight simulator or driving a race car with the WASD keys. Today, gamers expect more realism, and [600,000 milliliters] is no different. At first, he upgraded his race car driving chair and put on VR goggles. But watching the world whiz by in VR is you can’t feel the wind on your face. Armed with a 3D printer, some software, and some repurposed PC fans, he can now feel the real wind in virtual reality. You can see the build in the video, below.

The electronics are relatively straightforward and there is already software available. The key, though, is the giant 3D printed ducts that direct the airflow. These are big prints, so probably not for some printers, but printers are getting bigger every day. The fan parts are from Thingiverse, but the enclosures are custom and you can download them from the blog post.

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Road Pollution Doesn’t Just Come From Exhaust

Alumni from Innovation Design Engineering at Imperial College London and the Royal College of Art want to raise awareness of a road pollution source we rarely consider: tire wear. If you think about it, it is obvious. Our tires wear out, and that has to go somewhere, but what surprises us is how fast it happens. Single-use plastic is the most significant source of oceanic pollution, but tire microplastics are next on the naughty list. The team calls themselves The Tyre Collective, and they’re working on a device to collect tire particles at the source.

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Adaptive Infotainment Plays Tunes To Match Your Dangerous Driving

Part of the fun of watching action movies is imagining yourself as the main character, always going on exciting adventures and, of course, being accompanied by the perfect soundtrack to score the excitement and drama of your life. While having an orchestra follow you around might not always be practical, [P1kachu] at least figured out how to get some musical orchestration to sync up with how he drives his car, Fast-and-Furious style.

The idea is pretty straightforward: when [P1kachu] drives his car calmly and slowly, the music that the infotainment system plays is cool and reserved. But when he drops the hammer, the music changes to something more aggressive and in line with the new driving style. While first iterations of his project used the CAN bus, he moved to Japan and bought an old Subaru that doesn’t have CAN. The new project works on something similar called Subaru Select Monitor v1 (SSM1), but still gets the job done pretty well.

The hardware uses an Asus Tinkerboard and a Raspberry Pi with the 7″ screen, and a shield that can interface with CAN (and later with SSM1). The new music is selected by sensing pedal position, allowing him to more easily trigger the aggressive mode that his previous iterations did. Those were done using vehicle speed as a trigger, which proved to be ineffective at producing the desired results. Of course, there are many other things that you can do with CAN bus besides switching up the music in your car.

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What Makes A Hacker

I think I can sum up the difference between those of us who regularly visit Hackaday and the world of non-hackers. As a case study, here is a story about how necessity is the mother of invention and the people who invent.

Hackaday has overlap with sites like Pinterest and Instructables but there is one vital difference, we choose to create something new and beautiful with the materials at hand. Often these tools and techniques are very simple. We look to make things elegant by reducing the unnecessary clutter, not adding glitter. If something could be built with a 555 timer we will let you know. If there is a better choice for a processor, we will tell you.

My first real work commute was a forty-minute eastward drive every morning and a forty-minute westward drive every evening. This route pointed my car directly into the sun twice a day. Staring into a miasma of incandescent plasma for an hour and a half a day isn’t fun, and probably isn’t safe, but we can fix that.

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Control Thy LED

In a previous article, I discussed LEDs in general and their properties. In this write-up, I want to give some examples of driving LEDs and comparing a few of the most commonly used methods. There is no “one size fits all” but I will try and generalize as much as possible. The idea is to be able to effectively control the brightness of the LED and prolong their life while doing it. An efficient driver can make all the difference if you plan to deploy them for the long-haul. Let’s take a look at the problem and then discuss the solutions. Continue reading “Control Thy LED”

Grand Theft Auto V Used To Teach Self-Driving AI

For all the complexity involved in driving, it becomes second nature to respond to pedestrians, environmental conditions, even the basic rules of the road. When it comes to AI, teaching machine learning algorithms how to drive in a virtual world makes sense when the real one is packed full of squishy humans and other potential catastrophes. So, why not use the wildly successful virtual world of Grand Theft Auto V to teach machine learning programs to operate a vehicle?

Half and Half GTAV Annotation ThumbThe hard problem with this approach is getting a large enough sample for the machine learning to be viable. The idea is this: the virtual world provides a far more efficient solution to supplying enough data to these programs compared to the time-consuming task of annotating object data from real-world images. In addition to scaling up the amount of data, researchers can manipulate weather, traffic, pedestrians and more to create complex conditions with which to train AI.

It’s pretty easy to teach the “rules of the road” — we do with 16-year-olds all the time. But those earliest drivers have already spent a lifetime observing the real world and watching parents drive. The virtual world inside GTA V is fantastically realistic. Humans are great pattern recognizers and fickle gamers would cry foul at anything that doesn’t analog real life. What we’re left with is a near-perfect source of test cases for machine learning to be applied to the hard part of self-drive: understanding the vastly variable world every vehicle encounters.

A team of researchers from Intel Labs and Darmstadt University in Germany created a program that automatically indexes the virtual world (as seen above), creating useful data for a machine learning program to consume. This isn’t a complete substitute for real-world experience mind you, but the freedom to make a few mistakes before putting an AI behind the wheel of a vehicle has the potential to speed up development of autonomous vehicles. Read the paper the team published Playing for Data: Ground Truth from Video Games.

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