The process of realizing autonomous driving

The process of achieving autonomous driving can be simply described as from perception, decision-making to execution. Perception is the collection of information from the vehicle itself and the outside world through various sensors. Decision-making is the car's computing unit that analyzes the information obtained above according to specific algorithms and makes decisions suitable for the current situation. The next step is execution.

Algorithms are extremely important in the whole process. As one of the important application scenarios of artificial intelligence technology, the realization of autonomous driving technology is inseparable from the large-scale deployment of algorithms, including feature extraction from the perception link to the decision-making of neural networks. These all require algorithm improvements to improve the accuracy of obstacle detection and decision-making capabilities in complex scenarios.

AI algorithms are the most critical part of supporting autonomous driving technology. Currently, mainstream autonomous driving companies have adopted machine learning and artificial intelligence algorithms to achieve this. Massive data is the basis of machine learning and artificial intelligence algorithms. Through the data obtained from the sensors, V2X facilities and high-precision map information mentioned above, as well as the collected driving behavior, driving experience, driving rules, cases and surrounding environment data information, the continuously optimized algorithm can identify and ultimately plan routes and manipulate driving.

From a technical perspective, autonomous driving domain algorithms can be divided into perception algorithms, fusion algorithms, decision algorithms, and execution algorithms. Perception algorithms convert sensor data into machine language for the scene the vehicle is in, including object detection, recognition and tracking, 3D environment modeling, object motion estimation, etc.

The core task of the fusion algorithm is to unify the dimensions of data obtained by different sensors based on images or point clouds. As L2+ autonomous driving increases the requirements for multi-sensor fusion accuracy, the fusion algorithm will gradually move forward (front fusion), and its level will gradually move forward from back-end components such as domain controllers to the sensor level, completing fusion inside the sensor to improve data processing efficiency.

The decision-making algorithm gives the final behavioral action instructions based on the output results of the perception algorithm, including behavioral decisions such as following, stopping and chasing the car, as well as action decisions such as steering and speed of the car, path planning, etc.

Autonomous driving is divided into L0-L5 levels according to the degree of automation functions. L1-L3 mainly serve as auxiliary driving functions. After L4 level, vehicle control can basically be handed over to the artificial intelligence system.

Different levels have different functions to implement and require different algorithms. For example, L1's ACC adaptive cruise control, LKA lane departure assist, AEB automatic braking, and BSM blind spot monitoring require the use of ACC system control algorithm, LDW lane departure warning algorithm, LKA lane keeping assist algorithm, AEB automatic braking algorithm, and BSM blind spot monitoring algorithm.

For example, L3+ requires TJP traffic jam assistance algorithm, HWP highway assistance algorithm, urban road automatic driving algorithm, highway automatic driving algorithm, AVP automatic parking algorithm. L5 requires various automatic driving algorithms to realize corresponding functions.

Different manufacturers have different capabilities in providing algorithms. For example, traditional TIer1 manufacturers, such as Bosch, Continental, Desay SV, and some software algorithm manufacturers, can provide some algorithms for single functional modules, which can be better applied to L1-L2 assisted driving. Algorithm solution providers such as Momenta, Minieye, UISEE Technology, and ZongMu Technology can provide complete ADAS or autonomous driving solutions.