⚡ Why Is Self Driving Important For The Future

Wednesday, December 22, 2021 7:12:56 PM

Why Is Self Driving Important For The Future



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Self-Driving Cars: The Future of Transportation

Nonetheless, some of the people making that presentation may have failed to take into account that not everyone is fully literate in microprocessor design and engineering. I also have some theories about what this new chip can lead to. Full warning: this presentation is still quite technical, but I do try to explain the key points in plain English as well. First, we can see a general image of the board.

The board has complete redundancy, meaning that any system on the board can fail and the computer will continue operating as if nothing happened. On the right side of the board is where all the cameras plug into the board, and on the left side is where the power supply connects — as well as some input and output connectors. Tesla uses 2 processors for redundancy and for cross-referencing the results, not to increase performance.

Good analysis. This is a long time to a computer. Like a twin-engine plane, use both engines to max for normal operation, but can safely operate on just one. Under the processor and a bit to the left of the processors marked light blue are the flash memory chips that store the operating system. The capacity of each chip as of this moment is unknown, but considering that nowadays you can buy a micro-SD card with a capacity of GB, it could potentially be quite big. The processor is being fabricated by Samsung and this has also mistakenly led some people to believe that the RAM is also from Samsung, but that is actually not the case. If you take a really close look at the chip, you can see a little logo. Samsung does not put a logo like that on its RAM chips, but Micron does and its logo, especially the one it puts on chips, seems to very closely match what we see in this image.

LPDDR4 is also what is currently used in smartphones. Once you take off the heat spreader, the die is revealed to us, and Tesla has told us a lot of information about it. The size of the die is around mm2. It is actually a full system-on-a-chip SoC. Tesla explains the whole process by somewhat following the path that the data from the cameras take. This a hell of a lot more data than the currently installed sensors create. This then travels into the DRAM we discussed earlier, which is one of the first and main bottlenecks of the chip since this is the slowest component.

Then the data go back into the chip and through the image signal processor that can process 1 billion pixels per second roughly 8 Full HD p screens at 60 frames per second. This part of the chip turns the raw RGB data from the camera sensors into data that is actually useful in addition to enhancing the tone and removing noise. Then we finally get the most interesting part of the whole chip, the neural network processor, or NPU. The first step in the process is that the data gets stored in the SRAM array.

What does all of this mean, though? It means storage that is really fast but also expensive. Most consumer laptop and desktop processors have between MB of L3 cache. Tesla considers its large SRAM capacity to be one of its biggest advantages over any other kind of chip it could have potentially used. Keep in mind that if the cameras do indeed work at 60 frames per second and one combined frame in the SRAM could potentially equal 3. Tesla indicated during the Autonomy Day presentation that this is enough but could be better, and from that we gather that Tesla will likely improve it in its next-generation product. The neural network processor is an incredibly powerful tool. A lot of the data go through it, but some of the computational tasks have not yet been adjusted to work on a neural network processor or are not suitable for that kind of processor.

This is where the GPU comes in. Tesla indicated that the GPU currently performs some post-processing tasks, which could potentially include the creation of pictures and videos that are understandable for humans. However, from the way Tesla described the role of the GPU in its presentation, expect the next iteration of the chip to have a much smaller GPU. There are also some general-purpose processing tasks unsuitable to the neural processor that are done by the CPU. Although, a more accurate description would be to say that there are three 4-core CPUs in there.

Cortex A72 is an architecture from Since then, the A73, A75, and a few days ago even A77 architectures have been released. Elon and team explained it by saying that this was what was available when they started the design of the chip 2 years ago. Perhaps this was a cheaper option for Tesla, which would make sense if multithread performance is more important to them than single task performance, hence the inclusion of 3 older processors rather than one or two newer or more powerful ones.

In any case, the CPU performance on this chip is 2. Theoretically, if the chip were tasked to perform some other task in another scenario, it might be able to reach that 30 TOPs figure, but that is a pretty useless metric in this context. Worth remembering is that, when benchmarking a complex piece of software, it is all about the performance that specific software can realize.

This is why the best hardware is not always the hardware with the highest theoretical performance. In the past, we used to only have a general purpose processor with a numerical co-processor. Then we got the graphical co-processor, and now the NeuralNet co-processor. Although, ironically, in this case, the CPU is more of a co-processor to the neural processing unit. Basically, what Tesla did was create a specialized processor that is way better at an extremely specific task, but would suck at general purpose processing.

Tesla in this case already complains about the latency of information reaching the chip from the DRAM, which is right next to it. Waymo's parent company Alphabet has a shaky relationship with the EU, and it lacks the brand recognition and loyalty that its European competitors have. Though it began with gusto, Uber's self-driving car program is currently in limbo. After a fatal accident in Arizona in March , the state's governor suspended Uber's ability to test self-driving cars in the state. Uber had already shut down tests nationwide following the accident. Then, in May, Uber announced it was shutting down its self-driving car program in Arizona completely. It will continue tests in San Francisco, Toronto and Pittsburgh, whenever tests resume.

When Uber's tests begin again, they will be in a much more limited fashion than before. As far as when they start again, Uber CEO Dara Khosrowshahi hopes to see his autonomous fleet driving in the next couple months. The company is also undergoing an internal safety review. The preliminary NTSB report reveals that while the vehicle had an automatic emergency braking feature, this was disabled because the car was in "computer mode. Though the car detected it needed to make an emergency braking maneuver 1. You can read more on what is in the initial NTSB report into the fatal Uber self-driving car crash here.

What's more, Uber has developed an autonomous truck service that will make freighting goods across the country much easier for truck drivers. Despite the work that it's done in the self-driving car space, Uber has a big uphill battle before the public trusts its autonomous vehicles again. One way Uber is eyeing as a means for getting autonomous vehicles on the road without as great of safety concerns is by partnering with Waymo.

Uber's CEO has said the companies are in talks, trying to bring some of Waymo's vehicles to Uber's driverless car fleet. However, given Uber and Waymo's past legal battle over trade secret theft, the grounds for a new partnership seem shaky. Tesla Model X, Model X and Model 3 cars all feature the latest version of Autopilot , a sensor system of cameras, sonar and radar built for autonomous driving on highways. Tesla's AI can perform tasks like preemptively shift lanes before an exit or to avoid slower traffic, and can autosteer around more windy highways. As of early , Tesla owners had allegedly driven hundred of millions of miles in Autopilot mode. And, because Tesla scrapes data from all of its cars, it's able to gather information on apparent errors to improve Autopilot over time.

That dwarfs the mere millions of public road miles that most self-driving cars have achieved. Still, many drivers tend to treat Autopilot like a self-driving mode rather than as a driver assistance systems, which has led to serious accidents, including in recent months. One recent crash killed a Tesla Model X driver when his car crashed on a freeway in California.

The NTSB is still investigating the crash. The below tweet shows how its tech can pick up on potential hazards most humans might miss. Original video, authorisation from the owner. Essential, no one could predict the accident but the radar did and acted by emergency braking. Outside of these three major players, many other companies are maneuvering to accelerate public testing, or even launch for-profit driverless car services, in the next few years.

General Motors, the runner-up to Waymo in AI reliability, plans to start testing its cars in Manhattan this year. New York is something of an Everest for self-driving companies to climb: building an AI capable of navigating the city's traffic and hoards of pedestrians is no easy task. If the cars can pass this gauntlet, GM's AI could be powerful enough for the Chevy Cruise AV , a truly driverless car without a steering wheel or gas pedal. But, GM isn't going to tackle this challenge alone. Volkswagen, conversely, is braving the chaotic battleground known as parking garages for its testing.

At the Hamburg Airport in Germany, VW car owners can simply drop off their cars in front of the garage and activate a smartphone app; the car then self-drives to a free parking space, using its GPS and cameras to navigate. Eventually, VW has designs to make your driverless car maintain itself, and even do your chores. The company stated how its cars will be able to speak with city systems to find free parking, or drive themselves to gas stations or car washes for service.

Hyundai hopes to have its cars fully driverless on the road by , and Ford also aims to have its driverless AI and traffic-tracking technology up and running in the same year. Samsung recently got permission from the California DMV to test autonomous vehicles. And even Huawei has jumped into the game, showing off a self-driving car earlier this year that ran entirely off of camera data from a smartphone. Finally, Lyft hopes to beat Uber at its own game. Lyft launched its own self-driving division last year, and have since teamed up with Ford and acquired the help of an automotive parts supplier, Magna , for its self-driving car machinery.

With so many companies hoping to launch self-driving services and ramp up testing in the next couple of years, driverless car tech must be up to the challenge to avoid a rise in accidents as a result. Both Uber and Tesla have recently been embroiled in scandals surrounding their self-driving AI after two fatal accidents this year. In , when Autopilot was still newly implemented technology, a Tesla enthusiast fatally crashed into a trailer while Autopilot was engaged. At the time, there was awareness that Autopilot had trouble picking up trailers on its cameras, but nothing had been done to fix the issue before the crash. The agency warned that drivers using the system became too complacent to respond to any potential threats.

That pattern would somewhat repeat itself in a fatal accident, when a Tesla Model X driver crashed into a concrete barrier while using Autopilot. The NTSB is also investigating this incident, and expressed displeasure that Tesla released its own results of the crash before the NTSB could publicly make its own statement. To do otherwise would be unsafe.

April 2, Prior to this accident, an Uber car with driverless technology struck a pedestrian as she walked outside of a crosswalk at night. This fatal collision led to Uber suspending all of its self-driving operations indefinitely. As with Tesla, the NTSB investigation of the crash is still ongoing, though the agency's preliminary report into the accident has been issued. Some incredibly sad news out of Arizona. As for Google's most high-profile incident, it happened in March when a self-driving Lexus SUV attempted to make a turn in front of a bus, with the car's AI assuming the bus would slow down to allow it to do so. Most recently, a self-driving Waymo minivan was involved in an accident in May , in Chandler, Arizona.

The human driver behind the wheel suffered minor injuries. Waymo released footage of the incident, which makes it clear that neither the AI nor the human operator could have reasonably anticipated the crash. Waymo will probably face significant backlash if it does face a serious accident of its own after Krafcik's bold claim. Of course, we'll have to wait until authorities conclude their investigations into the recent self-driving car accidents before we can fully assess how safe the tech is and what steps need to be taken to avoid future accidents.

The history of the driverless car industry has been one of bold promises, high-profile fiascos, and general uncertainty about the future. A research team found that deep learning networks in self-driving cars are prone to make thousands of incorrect choices when faced with tricky scenarios. The researchers are hoping to develop a more complete test for self-driving car companies to check whether their AIs can navigate these problems. But, in the meantime, more accidents could be in store. Without protections in place, driverless cars could even become weaponized for potential attacks.

The researchers recommended that companies work with one another and with lawmakers to preempt potential hacking vulnerabilities. Will rivals like Waymo and Uber be willing to share such data, or will they hoard it? One can hope that companies will see the benefits of working together for the well-being of all. If self-driving cars do take off, though, we can expect a future where companies rely more frequently on autonomous tech, potentially at the expense of jobs. Amazon, for example, hopes to lower shipping costs by employing driverless delivery vehicles.

Of course, car manufacturers like GM and Ford will likely want to sell their self-driving cars to consumers directly, so they might lobby against such proposals.

Nicole Carman is a mental health blogger and advocate. Google says at Why Is Self Driving Important For The Future point, when it deems its software safe, it will start putting real people Why Is Self Driving Important For The Future the cars beyond Google engineers. Personal Narrative-African American Woman the best ways to manage Why Is Self Driving Important For The Future and negativity in your life.

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