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What does deep learning mean for autonomous vehicle?

Author:QINSUN Released in:2024-03 Click:24

Using deep learning in autonomous vehicle can help overcome various challenges, such as understanding the behavior of pedestrians, finding short routes, and accurately detecting people and objects.

According to a report, approximately 80% of road traffic accidents in 2018 were caused by human error. Therefore, one of the main goals of bringing autonomous vehicle into the mainstream is to eliminate the demand for human drivers and reduce the road fatality rate. The experiments conducted with autonomous vehicle undoubtedly show that the number of road casualties has been reduced to a certain extent.

However, many people still often see news about autonomous vehicle accidents, such as the Uber autonomous vehicle accident that killed a pedestrian in Arizona. The cause of the accident is said to be that autonomous vehicle cannot accurately detect and identify pedestrians. In order to minimize such accidents as much as possible, it is necessary to conduct extensive training on autonomous vehicles to accurately detect the presence of personnel and any other objects in their routes, which is the intervention of deep learning. The in-depth learning of autonomous vehicle can help them effectively classify and detect people or objects on the road and in the surrounding environment.

Deep learning is a subset of machine learning that uses artificial neural networks to mimic the complex functions of the human brain. Deep learning can classify objects more accurately without any human intervention. For example, suppose there are two people writing the number nine (9), but both of them write the numbers in different ways (one person writes nine, the others write nine, and there is no clear curve at the bottom). Unless all possible methods for writing the number nine are mastered, AI algorithms outside of deep learning networks will be difficult to detect, as both numbers represent nine despite their different shapes. Deep learning using deep neural networks can easily recognize both numbers as 9. The ability of deep learning to accurately classify different objects can solve some of the main challenges faced by autonomous vehicle.

Autonomous vehicle learn how to deal with some challenges in depth

Machine learning algorithms face the problem of feature extraction when training autonomous vehicle. Feature extraction requires programmers to tell algorithms what they should look for to make decisions. Therefore, the decision-making ability of machine learning algorithms largely depends on the programmer's insight. The functions of deep learning are different, eliminating the problem of feature extraction, thereby making the detection and decision-making of deep learning neural networks more accurate. Deep learning can improve the accuracy of detecting obstacles on the road and better decision-making ability, and can help to cope with many challenges faced by autonomous vehicle.

Understanding complex traffic behaviors

Driving is a process that involves complex interactions with other drivers and pedestrians. For example, if a cyclist intends to turn, he or she will make gestures to notify other nearby drivers. Then, the driver can slow down their vehicle to allow cyclists to turn. Human beings rely on universal intelligence for this social interaction. Moreover, through in-depth learning, autonomous vehicle are now likely to interact socially with other drivers and pedestrians. Deep learning neural network can help autonomous vehicle detect the navigation signals given by other drivers and pedestrians, and take appropriate measures to avoid any collision.

Detecting signs under weather conditions

Another major challenge for autonomous vehicle is weather conditions. Although this is an environmental challenge that no technology can fully solve, deep learning can solve problems under the climate. For example, during snowfall, signs on the road may be covered by snow. Moreover, for a period of time after snowfall, signs may only be partially visible. Using other AI algorithms, it will be difficult for autonomous vehicle to understand half of the signs on the signs. However, deep learning using neural networks can create a complete image of a sign from partially visible signs. Neural networks send incomplete symbols to the neural layer and then pass them on to the hidden layer to determine what the complete symbol should be. Based on output, neural networks can make decisions based on the signs on the sign.

Finding Short Travel Routes

All animals on Earth, including humans, can navigate and flexibly explore new areas in their surrounding environment. Due to the spatial behavior of neural circuits, their navigation becomes possible. The animal's brain navigates by drawing the surrounding environment on a regular hexagonal grid. These hexagonal patterns are helpful for navigation, similar to grid lines in maps. The neural pattern supports the assumption of vector based navigation. Vector based navigation allows the brain to calculate the distance and direction to the desired position.

Vector based navigation can be used to train deep learning neural networks to find short paths from point A to point B. By embedding the same grid pattern used by animal brains into the first layer, deep learning can calculate distance and direction to reach the destination. With vector based navigation and deep learning functions, autonomous vehicle can also detect the existence of any newly available shortcuts to reduce travel time.

Deep learning itself still needs to overcome many challenges

Although autonomous vehicle has many advantages, deep learning alone cannot make autonomous vehicle become an advanced intelligent transportation tool, because there are many obstacles hindering autonomous vehicle from going mainstream. With the help of deep learning, the accuracy of detecting objects can indeed be improved, but it comes at the cost of a large amount of data. Deep learning functionality based on data representation. The data is represented at different layers of the neural network, and then exported according to the data pattern. Due to the fact that the full functionality of deep learning is data-driven, training neural networks requires more data compared to other AI algorithms, making it difficult to create datasets for training them. Moreover, collecting the data required for training neural networks is also very time-consuming.

Another challenge in using deep learning neural networks is their black box problem. If the program makes a decision, the programmer can undo the decision to find out the reason for the program's decision. However, deep learning is not a traceable system, but rather processes data in hidden layers. Developers can only find the data input to the neural network and its output. However, they are unable to identify what processing has been done in the hidden layer to make a decision. Therefore, it is difficult to know the reasons for the failure of deep learning networks, as no one can trace back to the place where the failure occurred.

Sometimes, deep learning networks may not even be able to accomplish the tasks they originally intended to accomplish. It is difficult for neural networks to generalize in small image transformations like in different video frames. For example, according to a study, deep convolutional networks label baboons or mongooses as the same polar bear, depending on small changes in the background.

Autonomous vehicles are an experiment, and to this day, no one knows the results. Whether the deep learning of autonomous vehicle can drive it to the mainstream means of transportation depends on the further development of technology. Even if the challenge of deep learning is overcome, there are other obstacles in the way of autonomous vehicle. These cars are integrated with various technologies such as IoT devices to collect data, cloud computing to process data, and 5G to improve data transmission speed. Once these technologies can work together effectively to establish a good traffic ecosystem, autonomous vehicle can become the mainstream.