Electric cars, as a concept, were once not dissimilar from the flying car. Promised to be a big thing in the future, but hopelessly impractical in the here and now. However, in the last ten years, they’ve become a very real thing, with market share growing year on year as new models bring greater range and faster charging times.
Currently, if you want to use the Autopilot or Self-Driving modes on a Tesla vehicle you need to keep your hands on the wheel at all times. That’s because, ultimately, the human driver is still the responsible party. Tesla is adamant about the fact that functions which allow the car to steer itself within a lane, avoid obstacles, and intelligently adjust its speed to match traffic all constitute a driver assistance system. If somebody figures out how to fool the wheel sensor and take a nap while their shiny new electric car is hurtling down the freeway, they want no part of it.
So it makes sense that the company’s official line regarding the driver-facing camera in the Model 3 and Model Y is that it’s there to record what the driver was doing in the seconds leading up to an impact. As explained in the release notes of the June 2020 firmware update, Tesla owners can opt-in to providing this data:
Help Tesla continue to develop safer vehicles by sharing camera data from your vehicle. This update will allow you to enable the built-in cabin camera above the rearview mirror. If enabled, Tesla will automatically capture images and a short video clip just prior to a collision or safety event to help engineers develop safety features and enhancements in the future.
But [green], who’s spent the last several years poking and prodding at the Tesla’s firmware and self-driving capabilities, recently found some compelling hints that there’s more to the story. As part of the vehicle’s image recognition system, which usually is tasked with picking up other vehicles or pedestrians, they found several interesting classes that don’t seem necessary given the official explanation of what the cabin camera is doing.
If all Tesla wanted was a few seconds of video uploaded to their offices each time one of their vehicles got into an accident, they wouldn’t need to be running image recognition configured to detect distracted drivers against it in real-time. While you could make the argument that this data would be useful to them, there would still be no reason to do it in the vehicle when it could be analyzed as part of the crash investigation. It seems far more likely that Tesla is laying the groundwork for a system that could give the vehicle another way of determining if the driver is paying attention.
While we’re currently in an era of comparatively low gas prices, the last few decades have seen much volatility in the oil market. This can hit the hip pocket hard, particularly for those driving thirstier vehicles. Thankfully, modifications can help squeeze a few extra miles out of each gallon of dinosaur juice if you know what you’re doing.
The art of striving for the best fuel economy is known as hypermiling, and involves a broad spectrum of tricks and techniques to get the most out of a drop of fuel. Let’s dive in to how you can build a more efficient cruiser for getting around town.
Step 1: Know Thine Enemy
If you want to improve your fuel economy, the first step is to measure it. Without accurate measurement, it’s impossible to quantify any gains made or optimise for the best performance. For those with modern cars, it’s likely that there’s already a trip computer built into the dash. Using this to track your fuel economy is the easiest solution. Instantaneous modes are useful to help improve driving habits, while average modes are great for determining the car’s economy over time.
However, many older vehicles don’t have such features installed as stock. Thankfully, there’s a few ways to work around this. For those driving post-1996 vehicles outfitted with an OBD-II port, tools like Kiwi or Scangauge can often track fuel economy. Failing this, most fuel injected cars can be fitted with a device like the MPGuino that monitors fuel injection to calculate consumption. Fundamentally, all of these tools involve tracking the amount of fuel used per distance travelled. Factory tools and OBD-II gauges do it by using the car’s standard hardware, while the MPGuino splices in to speedometer signals and injector triggers to do the same thing with an Arduino. If you do decide to install a custom device, make sure you calibrate it properly, else your figures won’t bear much resemblance to what’s going on in reality.
Of course, as long as your car has a working odometer and a fuel tank that doesn’t leak, there’s always the pen-and-paper method. Simply reset the trip odometer to zero after filling the tank to the brim. Then, when refilling the tank, fill all the way to the top, and divide the miles driven by the gallons of fuel added back to the tank. This isn’t the most accurate method, as the nature of gas station pumps and automotive fuel tanks mean that tanks aren’t always accurately filled to the brim, due to air pockets and devices used to prevent overfilling. Despite this, it’s a handy way of getting some ballpark figures of your car’s performance over time.
Looking to give himself a competitive edge, racer [Douglas Hedges] wanted to come up with a system that could give him real-time feedback on how his current performance compared to his previous fastest lap time. Armed with a Raspberry Pi and some Python libraries, he set out to add a simple telemetry system to his car. But as is often the case with these kind of projects, things just started snowballing from there.
At the most basic level, his system uses GPS position and speed information to light up a strip of RGB LEDs on the dashboard: green means he’s going faster than the previous best lap, and red means he isn’t. Any interface more complex than that would just be a distraction while he focuses on the track. But that doesn’t mean the Raspberry Pi can’t collect data for future review after the race is over.
With the basic functionality in place, [Douglas] turned his attention to collecting engine performance data. It turned out the car already had some pre-existing equipment for collecting metrics such as the air-fuel ratio and RPM, which he was able to connect to the Raspberry Pi thanks to its use of a well documented protocol. On top of that he added a Labjack U3 data acquisition system which let him pull in additional information like throttle position and coolant temperature. Grafana is used to visualize all of this data after the race, though it sounds like he’s also considering adding a cellular data connection vehicle data can be streamed out in real-time.
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.
[Keith57000] started building the V10 engine back in 2013, after completing a 1/4 scale V8. The build is documented in a forum thread with lots of pictures of his beautiful craftsmanship. Most of the mechanical components were machined on a manual lathe and milling machine. No CNC, just lots of drawings and measurements, clever use of dividing heads, and careful dial reading. The engine also features electronic fuel injection with a MegaSquirt controller.
The rest of the car is just as impressive as the power plant. The chassis is bent tube, with machined brackets and carbon fiber suspension components. Two electric skateboard motors are added to give it a bit more power. The three speed gearbox is also custom, built with gears scavenged from a pit bike and angle grinder. It uses two small pneumatic pistons to do the shifting, with a clever servo mechanism that mechanically switches the solenoid valves. Check out all fourteen build videos on his channel for more details.
An amateur project of this complexity is never without speed bumps, which [Keith57000] details in the videos and build thread. It has taken seven years so far, but it is without a doubt the most impressive RC car we’ve seen. His skill with manual machine tools is something we rarely get to see in the age of CNC. We’re looking forward to the finished product, hopefully screaming around a track with a FPV cockpit.
The whole point of gaining the remote unlock ability for our cars was to keep us from suffering the indignity of standing there in the rain, working a key into the lock while the groceries get soaked. [Mattia Dal Ben] reports that even Teslas get the blues and don’t unlock reliably all the time, in spite of the price tag.
After programming a new J3A040-CL key card to match the car, getting the chip out was the easy part — just soak it in acetone until you can peel the layers apart. Then [Mattia] built a fresh antenna for it and wound it around the inside of a 3D printed back plate.
The hardest part seems to be the tuning the watch antenna to the resonant frequency expected by the car-side antenna. [Mattia] found that a lot of things mess with the resonant frequency — the watch PCB, casing, and even the tiny screws holding the thing together each threw it off a little bit.
Since the watch is less comfortable now, [Mattia] thought about making a new back from transparent resin, which sounds lovely to us. It looks as though the new plan is to move it to the front of the watch, with a resin window to show off the chip. That sounds pretty good, too. Check out the secret unlocking power after the break.