“Real Engineering” channel on self-driven cars

Self-Driven Cars

From the YouTube channel called “Real Engineering,” a source for the video post about self-driven cars, the reported talks about this through the exhibition of the new Tesler brand car. This was to engage their investors of the new features and autonomous driving capabilities. The post is called The Challenges of building a Self-driven Car, and it explains the different challenges incurred in the manufacturing of the self-drive cars. There are three primary challenges automakers like Tesla needs to overcome to succeed in replacing the human driver. The first of those is building a safe system. In order to replace human drivers, the self-driving car needs to be more reliable than the human driver (Nees, 65). To do so, they should quantify this plan regardless of them guaranteeing whether or not an accident might occur at any given time. From the Old Murphy’s Law that is always in play, we can start by quantifying how safe human drivers are. In the US, the current fatality rate is about one death per 1 million hours of driving. That includes humans being ignorant and crushing while drunk or looking at their phones.

Tesla car brand holds its vehicles to higher standards. Their self-driving vehicle needs to fail less than once every 1 million hours, and currently, that was not the case. Regardless, they do not have enough data to calculate an accurate statistic here. Uber is the self-driving vehicle needed a human to intervene around every 19 kilometers, meaning it failed every 19 kilometers, which makes Uber’s collision with a pedestrian who unfortunately passed away even more shocking (Volker and Handy, 37). Supporters of self-driving vehicles were quick to blame the pedestrian for stepping in front of the car in low light conditions. That did not put their dreams to a halt due to the mishap that occurred. The vehicle was using LIDAR sensors, which do not need lights to see. It did not attempt to slow down even after the human occupant, who was not paying attention, had noticed the imminent crash. Currently, most Tesla vehicles are capable of enhancing the drivers’ abilities, and it can take over the tedious task of maintaining lanes on highways monitor and match the speeds of surrounding vehicles and can even be summoned to an individual while they aren’t in the car. Those capabilities are impressive and, in some cases, even life-saving, but it is still a far reach from a fully self-driving vehicle. This is because it requires regular input from the driver to ensure they are paying attention and capable of taking over when needed. From the crash and according to data obtained from Uber, the vehicle first observed the pedestrian six seconds before impact with its radar and LIDAR sensors. At this point, it was traveling at 70 kilometers per hour, and it continued at the speed as the pedestrian and vehicle parts converged. Programming for safety and practicality in the practical self-driving vehicle is one of the challenges faced in the creation of self-driven cars.

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The computers classified system is seen struggling to identify what the object in its view is jumping from unidentified objects to carry to cyclist with no certainty in the trajectory path of the purpose and 1.3 seconds before the crash the vehicle recognized and needed to perform an emergency break but didn’t as it was programmed not to break if it would result in a deceleration. Instead, the human operator is expected to intervene, but regardless, the vehicle was not designed to alert the driver. A shocking design considering earlier statistic and the driver did intervene a second before impact by engaging the steering wheel and breaking, bringing the vehicle speeds, and this was too little and too late to save this person. Everything worked as programmed, and it was simply poor programming in my opinion, here at the internal computer was not programmed to deal with uncertainty, where a human would likely slow down when confronted with something on the road that it could not identify.


Tesla’s machine vision does a decent job in manufacturing self-driving cars, but there are plenty of gaps in their abilities. The software places bounding boxes around the objects it detects or categorizing them as cars, trucks, bicycles, and pedestrians. It labels each with a relative velocity to the vehicle and what lane it occupies. It highlights drivable areas marks the lane dividers and sets a projected path between them for now, this day, and allows autopilots to operate on highways. Still, it frequently struggles with more complicated scenes. Here a pedestrian is not detected, and it strives to tell if a roller skater is a bike or a pedestrian and eventually drives onto the wrong side of the road when there is a gap in lane dividers. But even if they managed to develop the perfect computer vision, programming the vehicle on how to handle every scenario is another hurdle. Often conflict with each other and take the AI program Tom Murphy developed to do something relatively simple to play Tetris (“YouTube”). This program worked brilliantly, but Tetris always wins the game is unbeatable, and you will eventually lose. When confronted with this option, the program did something to ensure it wouldn’t lose it pause the game. If a vehicle is programmed purely for safety, its safest option is not to drive. Driving is an inherently dangerous operation, and programming for the multitude of scenarios that can arise while driving is an insanely difficult task. It’s easy to say follow the rules of the road, and you will do fine. But the problem is, humans, don’t follow the laws of the road entirely.


Sometimes the computer will need to make difficult decisions, and may at times, need to make a decision that endangers the life of its occupants or people outside of the vehicle. That is just a natural byproduct of an inherently dangerous Task Force. If we continue improving on the technology, we could start to see road deaths promise while making taxi services drastically cheaper and free many people from the financial burden of purchasing a vehicle, Tesla is in a fantastic position to gradually update their software. As they master each scenario, they don’t need to create the perfect self-driving car out of the gates. And with this latest computer, they’re going to be able to continue improving their technology. Self-drive vehicles are useful in reducing human error made while driving. In the long-run, the programs help save human life and reduce casualties and, as such, are essential in use for the self-drive cars.

Future work directions

The most fantastic thing about software is that it is easily updatable, and brilliant have improved their software by allowing courses to be downloaded for offline use on iOS, so you can work on learning new things even on an underground train or a plane. Brilliant also recently released their fantastic course on Python coding called programming with Python. Python is one of the most widely used programming languages, and it is an excellent first language for new programmers. It can be used for everything from video games to data visualization to machine learning for self-driving vehicles. The use of these different programs will aid in the development of the self-drive car (“Programming Thinking and Vector Data Visualization” 80). Like city streets, accounting for all the unpredictability of driving requires an immense amount of training for a machine learning algorithm. And this is where Tesla’s data gives them an advantage, and it requires extensive training of the neural network. But the key takeaway you need is that the more data you have to train the neural network, the better it’s going to be. Tesla is more than aware of these problems and should gradually be improving on its software through firmware updates, adding functionality like stop line recognition, and this latest self-driving computer is going to radically increase the computer’s processing power, which will allow Tesla to continue adding functionality without jeopardizing refresh rates of information.



Works Cited

Nees, Michael A. “Safer than the average human driver (who is less safe than me)? Examining a popular safety benchmark for self-driving cars.” Journal of Safety Research, vol. 69, 2019, pp. 61-68.

“Programming Thinking and Vector Data Visualization.” Introduction to GIS Programming and Fundamentals with Python and ArcGIS®, 2017, pp. 77-96.

Volker, Jamey, and Susan Handy. “Projecting Reductions in Vehicle Kilometers Traveled from New Bicycle Facilities.” Transport Findings, 2019.

“YouTube.” YouTube, youtu.be/P8aTDusU7dw.

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