Waymo relies on the technological advantages of Alphabet and Google to introduce artificial intelligence technologies such as neural network systems and machine learning methods into the improvement of autonomous vehicles, greatly reducing the error rate of autonomous driving.
On the outskirts of Phoenix, Arizona, Waymo's minivans have completely eliminated the steering wheel and have achieved a full autopilot mode. These cars have "brain" control and a complex neural network. It sounds a bit scary, but the company says the system is absolutely safe.
Nowadays, if you buy a camera and a lidar sensor in your car, you can call it a self-driving car. However, for the field of autonomous driving, the most important and most important thing is how to make autonomous vehicles control the car like humans or better than human driving technology?
At Google's I/O Developers Conference, Waymo's CEO, John Krafcik, revealed a message: our self-driving cars look farther, more perceptive, and responsive. More sensitive than people.
Waymo has always been committed to making autonomous driving completely free of human dependence and fully automated.
Recently, they are working on an artificial intelligence project called "automated machine learning." In this project, they use neural network technology to improve autonomous driving technology.
Waymo's engineers not only model how cars recognize objects on the road, but also how human behavior affects cars. They use deep learning to interpret, predict, and respond to driving data on highways and in simulated environments.
Waymo is a leader in autonomous driving, with 6 million kilometers on the road and 5 billion miles in the simulated environment. In the process, Waymo collects a lot of data and information. Waymo has also partnered with Fiat Chrysler and Jaguar Land Rover. Dmitri Dolgov, Waymo's chief technology officer, said that autonomous driving is very demanding in terms of performance and accuracy, and driving experience in this area is important.
The concept of deep learning stems from the study of artificial neural networks. A multilayer perceptron with multiple hidden layers is a deep learning structure. Deep learning combines low-level features to form more abstract high-level representation attribute categories or features to discover distributed feature representations of data.
Deep learning is also a kind of machine learning. It is an excellent solution to improve the sensing ability and performance of autonomous driving vehicles by using different sensing layers of neural networks to analyze different abstract data.
Experts from Google's Brain team have also worked with engineers at Dolgoff to develop ways to improve the accuracy of autonomous driving.
Previously, the automatic driving system had a high error rate and could not distinguish cars, traffic signals and newcomers. In real-time situations, the corresponding speed is slower.
In order to improve this problem, Google has made great efforts in improving the recognition ability of the system. AI engineers combine machine learning with neural network technology and apply it to autonomous driving technology to exploit the technological advantages of both.
Dolgov said that people can distinguish between cats and dogs at a glance, but it is much more complicated to let people tell the reason for this conclusion. The same is true for deep learning. It is much simpler for the church machine to pick a pedestrian from the sensor data than to tell the principle and coding.
Waymo uses automated programs and different people's label classifications to train the system's neural network. After the training is completed, these huge databases need to be cut and compressed and applied in a real driving environment. This process, like the compression of images, is critical to the construction of a global autonomous driving system infrastructure.
It is easy to make autonomous vehicles able to respond to pedestrians passing by during the day, but it is relatively difficult to detect and react to a pedestrian crossing the road.
What if the pedestrian stops at the central branch of the road? Waymo's self-driving cars respond cautiously because pedestrians generally stand in the central lane. But what if there is no central branch area? Waymo's car can also recognize the abnormal behavior of pedestrians, and try to slow down and let the pedestrians go through.
Through the embedding of machine learning, Waymo can identify pedestrians' normal and irregular behaviors and prevent pedestrians from colliding with pedestrians when they cross the road.
In addition, neural network systems require a large amount of data and technology to support and train. As a subsidiary of Alphabet, Waymo can use Google's data center to train its neural network system. Specifically, Waymo can use Google's powerful cloud computing hardware system, the TPU (tensor processing units), which is enough to support Waymo's great technology development plan. Usually, this work is done by commercial GPUs, usually Nvidia. But in recent years, Google has also developed its own hardware and software systems. In contrast, TPUs are much faster than CPUs.
If Waymo is able to build a machine learning model and have a neural network system capable of driving on unclear streets, then Waymo's range of self-driving cars will not be limited to the suburbs of Phoenix, but more open. path of. This year, Waymo also plans to establish a fully unmanned taxi business in Arizona, USA.
5.08MM Wire To Board Connectors
5.08MM Wire To Board Connectors
5.08 mm Wire to Board connectors are avialable in different terminations and sizes intended for use on a variety of applications. These connectors provide power and signal with different body styles, termination options, and centerlines. To find the wire to board set required, click on the appropriate sub section below.
5.08mm Wire To Board Connectors Type
5.08mm Terminal
5.08mm Housing
Pitch 5.08mm Wafer Right Angle&SMT Type
5.08mm Wafer Straight Type
5.08mm Wafer SMT Type
5.08mm Wafer Right Angle Type
5.08mm Wafer Single Row Straight Type
5.08mm Wafer Single Row SMT Type
5.08mm Wafer Double Row Straight Type
5.08mm Wafer Double Row SMT Type
5.08mm Wafer Double Right Angle Type
5.08mm Wafer Double Right Angle&SMT Type
5.08mm Wire To Board Connectors Advantages
Small electronics with high current requirements
One of the strongest advantage of the Micro Fit series is its applicability in delivering the high current requirement in electronics with supplying high power.
Safe and Reliable
ensure safety, system protection and performance with its bonded metallic conduits and multiple grounding points preventing fire hazards, component damage, overheating and possible electrocution.
ROHS Compliant
The product does not contain restricted chemicals in concentrations not complying with ROHS standards. Thus, for its components, the products can be worked upon at high temperatures required by lead-free soldering.
5.08 mm Wire To Board Connectors Application
Motherboard Connectors
The Motherboard of a computer holds together many of the crucial components such as the central processing unit (CPU), memory and connectors for input and output devices. Microfit connectors ensure high performance electronic system in quality motherboards.
You can easily find Scondar`s Microfit in most ITX Motherboards, in which its functionality is best described for solid performance in a small footprint, and low power consumption (less than 100 Watts).
Printer Connectors
The 5.08mm Microfit can also be seen in most printers
Automotive PC Connectors
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Solar Panels
Microfit can also be commonly found in a solar array. A solar array is connected to each other, and to inverters through connectors to produce a successful flow of electricity.
5.08MM Wire To Board Connectors
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