Electric vehicles are fertile ground for innovation because the availability of suitable motors, controllers, and power sources makes experimentation accessible even to hobbyists. Even so, [John Dingley] has been working on such vehicles since about 2009, and his latest self-balancing electric unicycle really raises the bar by multiple notches. It sports a monstrous 3000 Watt brushless hub motor intended for an electric motorcycle, and [John] was able to add numerous touches such as voice feedback and 1950’s styling using surplus aircraft and motorcycle parts. To steer, the frame changes shape slightly with help of the handlebars to allow the driver’s center of gravity to shift towards one or the other outer rims of the wheel. In a test drive at a deserted beach, [John] tells us that the bike never went above 20% power; the device’s limitations are entirely by personal courage. Watch the video of the test, embedded below.
They say necessity is the mother of invention. But if the thing you need has already been invented but is extremely expensive, another mother of invention might be budget overruns. That was the case when [klinstifen]’s local government decided to put in countdown clocks at bus stops, at a whopping $25,000 per clock. Thinking that was a little extreme, he decided to build his own with a much smaller price tag.
The project uses a Raspberry Pi Zero W as its core, and a 16×32 RGB LED matrix for a display. Some of the work is done already, since the bus system has an API that is readily available for use. The Pi receives the information about bus schedules through this API and, based on its location, is able to determine the next bus arrival time and display it on the LED matrix. With the custom 3D printed enclosure and all of the other material, the cost of each clock is only $100, more than two orders of magnitude less expensive.
Hopefully the local government takes a hint from [klinstifen] and decides to use a more sane solution. In the meantime, you might be able to build your own mass transit clock that you can use inside your own house, rather than at the train station, if you’re someone who has a hard time getting to the bus stop on time.
[Revanth Kailashnath] writes in to tell us about an interesting project he and his team have been working on for their “Real Time Embedded Programming” class at the University of Glasgow. Intended to combat the harsh and dangerous winters in Glasgow, their system uses a Raspberry Pi and a suite of sensors to automatically deploy a brine solution to streets and sidewalks. While the project is still only a proof of concept and hasn’t been deployed, the work the team has done so far runs the gamut from developing their own PCBs to creating a web-based user interface.
The core idea is simple. If the conditions are right for ice to form, spray salt water. Using salt water is a cheap and safe way of clearing and preventing ice as it simply drops the temperature at which water freezes. The end result is that the ice won’t form until it gets down to 10F (-12C) or so. Not a perfect solution, but it can definitely help. Of course, you don’t want to spray people with salt water as they pass by, so there’s a bit more to it than that.
Using the venerable DHT22 sensor the team can get the current temperature and humidity, which allows them to determine when it’s time to start spraying. But to prevent any wet and angry pedestrians, a HC-SR501 PIR motion sensor is used. If the system sees motion it will stop for a while to let the activity quiet down.
Monitoring the sensors and controlling the pump is done by a daemon written in C++, which also logs data to an SQL database, which in turn feeds their PHP web interface. In the video after the break, [Revanth] demonstrates how the system is constantly making decisions based on the input of the various sensors. Environmental data and motion is analysed every few seconds to provide a real-time solution.
Self-driving cars have been in the news a lot in the past two weeks. Uber’s self-driving taxi hit and killed a pedestrian on March 18, and just a few days later a Tesla running in “autopilot” mode slammed into a road barrier at full speed, killing the driver. In both cases, there was a human driver who was supposed to be watching over the shoulder of the machine, but in the Uber case the driver appears to have been distracted and in the Tesla case, the driver had hands off the steering wheel for six seconds prior to the crash. How safe are self-driving cars?
Trick question! Neither of these cars were “self-driving” in at least one sense: both had a person behind the wheel who was ultimately responsible for piloting the vehicle. The Uber and Tesla driving systems aren’t even comparable. The Uber taxi does routing and planning, knows the speed limit, and should be able to see red traffic lights and stop at them (more on this below!). The Tesla “Autopilot” system is really just the combination of adaptive cruise control and lane-holding subsystems, which isn’t even enough to get it classified as autonomous in the state of California. Indeed, it’s a failure of the people behind the wheels, and the failure to properly train those people, that make the pilot-and-self-driving-car combination more dangerous than a human driver alone would be.
A self-driving Uber Volvo XC90, San Francisco.
You could still imagine wanting to dig into the numbers for self-driving cars’ safety records, even though they’re heterogeneous and have people playing the mechanical turk. If you did, you’d be sorely disappointed. None of the manufacturers publish any of their data publicly when they don’t have to. Indeed, our glimpses into data on autonomous vehicles from these companies come from two sources: internal documents that get leaked to the press and carefully selected statistics from the firms’ PR departments. The state of California, which requires the most rigorous documentation of autonomous vehicles anywhere, is another source, but because Tesla’s car isn’t autonomous, and because Uber refused to admit that its car is autonomous to the California DMV, we have no extra insight into these two vehicle platforms.
Nonetheless, Tesla’s Autopilot has three fatalities now, and all have one thing in common — all three drivers trusted the lane-holding feature well enough to not take control of the wheel in the last few seconds of their lives. With Uber, there’s very little autonomous vehicle performance history, but there are leaked documents and a pattern that makes Uber look like a risk-taking scofflaw with sub-par technology that has a vested interest to make it look better than it is. That these vehicles are being let loose on public roads, without extra oversight and with other traffic participants as safety guinea pigs, is giving the self-driving car industry and ideal a black eye.
If Tesla’s and Uber’s car technologies are very dissimilar, the companies have something in common. They are both “disruptive” companies with mavericks at the helm that see their fates hinging on getting to a widespread deployment of self-driving technology. But what differentiates Uber and Tesla from Google and GM most is, ironically, their use of essentially untrained test pilots in their vehicles: Tesla’s in the form of consumers, and Uber’s in the form of taxi drivers with very little specific autonomous-vehicle training. What caused the Tesla and Uber accidents may have a lot more to do with human factors than self-driving technology per se.
You can see we’ve got a lot of ground to cover. Read on!
Few would question the health benefits of ditching the car in favor of a bicycle ride to work — it’s good for the body, and it can be a refreshing relief from rat race commuting. But it’s not without its perils, especially when one works late and returns after dark. Most car versus bicycle accidents occur in the early evening, and most are attributed to drivers just not seeing cyclists in the waning light of day.
To decrease his odds of becoming a statistics and increase his time on two wheels, [Dave Schneider] decided to build a better bike light. Concerned mainly with getting clipped from the rear, and having discounted the commercially available rear-mounted blinkenlights and wheel-mounted persistence of vision displays as insufficiently visible, [Dave] looked for ways to give drivers as many cues as possible. Noticing that his POV light cast a nice ground effect, he came up with a pavement projecting display using four flashlights. The red LED lights are arranged to flash onto the roadway in sequence, using the bike’s motion to sweep out a sort of POV “bumper” to guide motorists around the bike. The flashlight batteries were replaced with wooden plugs wired to the Li-ion battery pack and DC-DC converter in the saddle bag, with an Arduino tasked with the flashing duty.
The picture above shows a long exposure of the lights in action, and it looks very effective. We can’t help but think of ways to improve this: perhaps one flashlight with a servo-controlled mirror? Or variable flashing frequency based on speed? Maybe moving the pavement projection up front for a head-down display would be a nice addition too.
We’ve all heard the complaints from oldsters: “Cars used to be so simple that all you needed to fix them was a couple of wrenches and a rag. Now, you need a computer science degree to even pop the hood!” It’s true to some extent, but such complexity is the cost of progress in the name of safety and efficiency. And now it seems this complexity is coming way down-market, with this traction control system for a Power Wheels Lamborghini.
While not exactly an entry-level model from the Power Wheels line of toddler transportation, the pint-sized Lamborghini Aventador [Jason] bought for his son had a few issues. Straight from the factory, its 6-volt drivetrain was a little anemic, with little of the neck-snapping acceleration characteristic of an electric drive. [Jason] opted to replace the existing 6-volt drive with a 12-volt motor and battery while keeping the original 6-volt controller in place. The resulting rat’s nest of relays was unsightly but sufficient to see a four-fold increase in top speed.
With all that raw power sent to only one wheel, though, the Lambo was prone to spinouts. [Jason] countered this with a traction control system using optical encoders on each of the rear wheels. A NodeMCU senses speed differences between the wheels and controls the motor through an H-bridge to limit slipping. As a bonus, a smartphone app can connect to the Node for in-flight telemetry. Check out the build and the car being put through its paces by the young [Mr. Steal Your Girl] in the video below.
New York City’s L train carries about 400,000 passengers a day, linking Manhattan and Brooklyn and bringing passengers along 14th Street, under the East River, and through the neighborhoods of Williamsburg, Bushwick, Ridgewood, Brownsville, and Canarsie. About 225,000 of these passengers pass through the Canarsie Tunnel, a two-tube cast iron rail tunnel built below the East River between Manhattan and Brooklyn in 1924. Like many other New York City road and subway tunnels, the Canarsie Tunnel was badly damaged when Hurricane Sandy’s storm surge inundated the tubes with million of gallons of salt water. Six years later, the impending closure of the tunnel is motivating New Yorkers to develop their own ambitious infrastructure ideas.
Electric vehicles are fertile ground for innovation because the availability of suitable motors, controllers, and power sources makes experimentation accessible even to hobbyists. Even so, [John Dingley] has been working on such vehicles since about 2009, and his latest self-balancing electric unicycle really raises the bar by multiple notches. It sports a monstrous 3000 Watt brushless hub motor intended for an electric motorcycle, and [John] was able to add numerous touches such as voice feedback and 1950’s styling using surplus aircraft and motorcycle parts. To steer, the frame changes shape slightly with help of the handlebars to allow the driver’s center of gravity to shift towards one or the other outer rims of the wheel. In a test drive at a deserted beach, [John] tells us that the bike never went above 20% power; the device’s limitations are entirely by personal courage. Watch the video of the test, embedded below.
