Invented by Soryoung KIM, LG Electronics Inc

The Market for Control Devices for Autonomous Vehicles and Methods of Controlling Them As technology continues to advance, the concept of autonomous vehicles is becoming increasingly popular. These self-driving cars have the potential to revolutionize the way we travel, making our roads safer and more efficient. However, for autonomous vehicles to become a reality, there is a need for sophisticated control devices and methods to ensure their safe and effective operation. The market for control devices for autonomous vehicles is rapidly growing, with various companies and researchers investing heavily in developing cutting-edge technology. These control devices are responsible for managing the complex systems within autonomous vehicles, including navigation, obstacle detection, and decision-making algorithms. One of the key components of control devices for autonomous vehicles is the sensor array. These sensors, such as cameras, lidar, radar, and ultrasonic sensors, provide real-time data about the vehicle’s surroundings. This information is crucial for the vehicle to make informed decisions and navigate safely through various road conditions. Another important aspect of control devices is the software algorithms that process the sensor data and make decisions based on it. These algorithms are designed to analyze the environment, detect obstacles, and determine the best course of action for the vehicle. They also take into account factors such as traffic rules, road conditions, and the vehicle’s own capabilities. In addition to the hardware and software components, control devices for autonomous vehicles also require a robust communication system. This allows the vehicle to interact with other vehicles, infrastructure, and the cloud, enabling it to share information and coordinate its actions with other entities on the road. This communication system is essential for ensuring the safety and efficiency of autonomous vehicles in a connected environment. The methods of controlling autonomous vehicles are constantly evolving as researchers and engineers strive to improve their performance. Machine learning and artificial intelligence techniques are being employed to enhance the decision-making capabilities of these vehicles. By continuously learning from real-world data, autonomous vehicles can adapt to changing road conditions and become more efficient over time. The market for control devices for autonomous vehicles is not limited to traditional automotive companies. Tech giants like Google, Apple, and Tesla are heavily invested in developing their own autonomous vehicle technologies. Additionally, startups and research institutions are also actively contributing to this market, bringing innovative solutions and pushing the boundaries of autonomous vehicle control. However, there are still challenges to overcome in the market for control devices for autonomous vehicles. Safety remains a top concern, as any failure in the control system can have severe consequences. Ensuring the reliability and robustness of these devices is crucial to gain public trust and widespread adoption of autonomous vehicles. Regulatory frameworks and standards are also essential to govern the development and deployment of autonomous vehicles. Governments around the world are working on establishing guidelines and regulations to ensure the safe operation of these vehicles on public roads. In conclusion, the market for control devices for autonomous vehicles is expanding rapidly, driven by advancements in technology and the growing demand for safer and more efficient transportation. The development of sophisticated control devices and methods of controlling autonomous vehicles is crucial to realize the full potential of this transformative technology. As the market continues to evolve, it is expected that autonomous vehicles will become a common sight on our roads in the near future.

The LG Electronics Inc invention works as follows

The disclosed method for controlling an autonomous vehicle includes: generating driving data by merging meta-information, including location-based information and image information, receiving an object detection algorithms selected based upon the meta-information, setting a path based on that object detection algorithm while monitoring the main object which has appeared at a location indicated by the location information and, when dangerous object information from a server is received, resetting the path to avoid a dangerous object. The present invention can include an autonomous vehicle, user terminals, and servers. These may work in conjunction with a UAV, Augmented Reality device (AR), Virtual Reality device (VR), and devices related to 5G services.

Background for Control device for autonomous vehicle and method of controlling it

Field of Invention

The present invention is a method of controlling an autonomous vehicle as well as a device for controlling it. It also relates to the reduction of algorithm resources and computation times when detecting an object.

Related Art

Vehicles can be classified as internal combustion engines, external combustion engines, gas turbine vehicles or electric vehicles.

The development of an autonomous car capable of driving itself without the need for a human driver is currently in full swing.

To replace the human perception capabilities, different sensors, such as a sensor infrared, radar, camera, etc., are used. They are used. The camera can replace the human eye and capture an image of driving conditions of a car. The autonomous vehicle also analyzes the image captured by the camera, and then performs autonomous driving on the basis of the image analyzed.

The present invention is a method of control and a device for detecting an object more quickly in an image taken by an autonomous vehicle.

The present invention is aimed at providing a control method, and a device that can reduce the algorithm resources required by an autonomous vehicle to detect an object in an image.

The present invention is a method of controlling and a device that allows an image to be quickly analyzed and an algorithm to be reduced. It also makes it possible to handle an emergency efficiently.

The object detection algorithm is used to set a driving route while monitoring the main object in the place indicated by location information.

Meta information can be used to generate driving information that includes time and weather data.

The object detection algorithm can be used to detect an object in real-time that appears at a location indicated by the position information.

The object detection algorithm can be used to detect an object in real-time that appears at a location indicated by the information about the location and within a period of time indicated by information about the time.

The object detection algorithm can also include an algorithm to detect an auxiliary object that guides traffic rules observation while the vehicle is traveling.

The receiving of an object-detection algorithm can include the following: extracting the location data from driving information by a server; preparing a base station by the server in which each piece of location info is stored; searching the database by the client for an algorithm that matches the location data; and receiving the algorithm by the vehicle from the server.

The server can prepare the database in several steps: first, selecting an object-detection algorithm to detect the main object that was extracted in the first stage; second, matching the algorithm selected in the second phase with each piece received of location data from the other vehicle; and finally, storing the algorithm matched.

The server can also update the object detection algorithm using driving data from other vehicles.

The updating of the object-detection algorithm can include: detecting and tagging new objects based on driving information from other vehicles, updating the main object based upon the correlation between the driving path and the location information and the appearance of the objects using a deep learning method derived from the tagging data.

The server can search for object detection algorithms that are generated using the driving data received from other vehicles in real-time.

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