This website uses cookies. By using this site, you consent to the use of cookies. For more information, please take a look at our Privacy Policy.

Automatic Driving L1-L5 Technology Difference

Jan 03, 2023      View: 318

Automatic Driving L1-L5 Technology Difference

Definition of Self-driving

A self-driving car, also known as a driverless, computer-driven, and wheeled mobile robot, is a kind of intelligent networked car that realizes unmanned driving through the computer system. The self-driving car relies on artificial intelligence, visual computing, radar, monitoring devices and a global positioning system to work together so that the computer can do vehicle-road cooperation without human active operation and operate the motor vehicle automatically and safely.

The Overall Framework of Autonomous Driving

The overall technology framework of autonomous driving can be divided into three major layers: perception, analysis, and application.

The perception layer replaces the human eyes and collects driving information involved in the driver's driving process through sensors (LIDAR, cameras, millimetre wave radar, high precision maps, etc.).

The analysis layer replaces the human brain to make calculations and develop corresponding control strategies through the acquired information.

The application layer replaces the human hands and feet to execute the received control strategies, which include acceleration and deceleration, steering, etc.

The Difference Between Autonomous Driving L1-L5 Technologies

According to the standards set by the Society of Automotive Engineers (SAE), autonomous driving levels are divided into 0-5. Level 0 is no driving automation, Level 1 is driving support, Level 2 is partial driving automation, Level 3 is conditional driving automation, Level 4 is advanced driving automation, and Level 5 is full driving automation.

Level 1 and Level 2 are positioned at Advanced Driver Assistance Systems (ADAS), not autonomous driving, and Level 3 and above correspond to autonomous driving.

L1 level: The driver still controls the vehicle and has some primary driving assistance functions.

L2 level: certain driving tasks can be done automatically, and after processing and analysis, the vehicle state can be adjusted automatically, like Tesla's lane-keeping function belongs to this level, which can control acceleration and deceleration in addition to the steering wheel.

L3 level: In autonomous driving, the vehicle can achieve automatic acceleration, deceleration and steering in a specific environment without the driver's operation, but the driver must be ready to take over the vehicle at any time during the automatic vehicle driving. When taking over the vehicle, the system will make prompt the driver.

L4 and L5 levels:?L4 has said goodbye to the driver.?The vehicle's intelligent automation has been completely ready to take over.?The driver can sleep or take over the vehicle's lead anytime.

L5 level: to achieve fully automatic driving, not limited by ODD, in principle, in any situation, can achieve automatic driving.

The common definition is "an automated system that continuously performs all dynamic driving tasks without limitation and responds to difficult-to-continue operations. If it is difficult to continue, the user requests intervention. No response is expected." "Sustainable unlimited" means that it is not affected by ODD and needs to respond without relying on human intervention, even when it is difficult to continue operation.

What problems are being solved by autonomous driving technology

The core and most difficult problem that autonomous driving needs to solve is "perception"; in other words, the better the system's perception of the surrounding driving environment, the stronger the integrated capability of autonomous driving; from here, the industry is divided into two major schools of thought: one is a pure vision solution; the other wants to incorporate as many The other is to add as many sensor fusion cases as possible.

Let's not discuss which path is the right one because it will likely achieve the same result in the future. However, no matter which path is taken, it requires deep learning of massive amounts of data, that is, the training of neural networks, to achieve the so-called fully autonomous driving, which is the only way.

Previous: Introduction of Vehicle-Mounted Intrusion Detection and Prevention System

Next: Introduction to the In-vehicle Camera Industry