What is a pure visual intelligent driving solut

1、 Introduction
In the current booming development of autonomous driving technology, pure visual intelligent driving solutions, as an emerging and highly anticipated technological route, are gradually changing people’s perception of autonomous driving. It abandons the traditional approach of relying on multi-sensor fusion such as LiDAR, and only relies on cameras and advanced algorithms to achieve intelligent driving functions of vehicles. The emergence of this solution not only brings new breakthroughs in technology, but also demonstrates unique advantages in cost, system integration, and other aspects. This article will provide a comprehensive and in-depth analysis of pure visual intelligent driving solutions, covering their basic concepts, technical principles, advantages and challenges, application cases, and future development trends.
2、 Basic concepts of pure visual intelligent driving solutions
（1） Definition
The pure visual intelligent driving solution is an autonomous driving perception solution based on camera and computer vision technology. It mainly relies on onboard cameras to capture real-time image information of roads, traffic signs, pedestrians, and other vehicles, and then uses computer vision algorithms to process and analyze these images, thereby achieving perception of the road environment and providing a basis for vehicle decision-making and control.
（2） Development background
With the rapid development of technologies such as artificial intelligence and deep learning, computer vision has made significant breakthroughs in areas such as image recognition and object detection. This provides a solid technical foundation for the development of pure visual intelligent driving solutions. At the same time, the high cost of sensors such as LiDAR, as well as the challenges of system complexity and data processing in multi-sensor fusion solutions, have prompted car manufacturers and technology companies to explore more economical and efficient autonomous driving solutions, giving rise to pure visual intelligent driving solutions.
3、 Technical principle of pure visual intelligent driving solution
（1） Perception layer

1. Camera hardware: The core hardware of the pure visual intelligent driving solution is the camera, which is equivalent to the “eyes” of the vehicle and can capture real-time image information of the surrounding environment. The number, resolution, field of view angle, and other parameters of cameras can affect their ability to perceive the environment. Generally speaking, vehicles are equipped with multiple cameras located at the front, rear, and sides of the vehicle to achieve comprehensive environmental perception. For example, some high-end car models may be equipped with 7 or even more high-definition cameras, which have high resolution and wide field of view, providing clearer and more comprehensive image data.
2. Image processing algorithm: The image data collected by the camera needs to be processed by image processing algorithms to extract useful information. These algorithms mainly include object detection, semantic segmentation, depth estimation, etc. Object detection algorithms can recognize various targets in images, such as vehicles, pedestrians, traffic signs, etc; Semantic segmentation algorithms can classify each pixel in an image to determine the boundaries and positions of different targets; The depth estimation algorithm can estimate the distance between the target and the vehicle. In recent years, deep learning algorithms have achieved great success in the field of image processing. Convolutional neural networks (CNN), transformers, and other algorithms have been widely used in pure visual intelligent driving solutions, greatly improving the accuracy and efficiency of image processing.
   （2） Positioning and Map Layer
3. SLAM and VO technologies: In order to accurately determine the vehicle’s location and establish a map of the environment, the pure visual intelligent driving solution adopts SLAM (Simultaneous Localization and Mapping) and VO (Visual Odometry) technologies. SLAM can simultaneously estimate the location of mobile devices and construct maps of the environment in unknown environments. It analyzes the image sequences captured by cameras, extracts feature points in the environment, and calculates the position and attitude of vehicles based on the changes in these feature points. VO estimates the camera’s motion trajectory by analyzing continuous image sequences, and then calculates the vehicle’s displacement. The combination of these two technologies enables the auto drive system system to achieve self positioning and navigation in a dynamic environment, and maintain good positioning capability even when the GPS signal is poor or does not exist.
4. Feature based matching algorithms: ORB (Oriented FAST and Rotating BRIEF) and SIFT (Scale Invariant Feature Transform) are two widely used feature detection and description algorithms. They help vehicles locate their relative position to known landmarks based on visual information. By identifying and matching key points in the environment, these algorithms can effectively find stable features in images and use these features to determine the precise location of vehicles on the map, which is crucial for achieving accurate vehicle positioning and navigation.
   （3） Decision and Planning Layer
5. Path planning algorithm: The task of path planning algorithm is to plan a safe path for the vehicle from the current position to the destination. Common path planning algorithms include A *, Dijkstra, and RRT. The A * algorithm is known for its efficient search strategy, which can quickly find the shortest path in complex environments; The Dijkstra algorithm ensures finding the exact shortest path between two points; RRT (Rapidly exploring Random Trees) is particularly adept at handling path planning problems in high-dimensional spaces, and can effectively explore unknown or partially known environments. These algorithms evaluate multiple possible route options, considering factors such as obstacle avoidance, road conditions, and driving efficiency, in order to select the optimal path, ensuring that autonomous vehicles can safely and efficiently reach their destination.
6. Behavioral decision-making algorithm: Behavioral decision-making algorithm determines the reaction of vehicles in specific situations, such as encountering traffic lights, pedestrians crossing the road, etc., through rules or machine learning methods. These algorithms comprehensively consider factors such as traffic rules, road conditions, and the behavior of other traffic participants to develop safe and reasonable driving strategies. By simulating the judgment process of human drivers or utilizing a large amount of training data, behavioral decision-making algorithms can enable autonomous vehicles to make appropriate decisions in complex traffic environments, ensuring safe and smooth driving. The decision-making and planning layer relies on the information provided by the perception layer and the location data provided by the positioning layer to develop safe and reasonable action strategies.
   （4） Control layer
   The control layer is responsible for executing instructions formulated by the decision-making and planning layers to ensure that vehicles travel according to the plan. PID controller and MPC (Model Predictive Control) are two key control technologies widely used in autonomous vehicles. The PID controller adjusts the speed and direction of the vehicle by adjusting three parameters: proportional, integral, and derivative, ensuring that the vehicle can smoothly follow the predetermined path. MPC utilizes predictive models to optimize future control actions, taking into account the vehicle’s dynamic characteristics and constraints to achieve more accurate and efficient path tracking. The combination of these two control strategies ensures that autonomous vehicles can travel steadily and accurately along the planned path.
   （5） Communication Technology
   V2X communication technology allows vehicles to communicate in real-time with other vehicles, infrastructure, and pedestrians, sharing critical information, significantly improving road safety and promoting traffic flow coordination. Through this communication method, vehicles can timely understand changes in the surrounding environment, such as traffic conditions ahead, potential danger situations, and the intentions of other vehicles, and then take corresponding preventive measures or adjust driving strategies to ensure safety and smoothness during the driving process. The application of V2X technology has greatly enhanced the perception range and decision-making ability of the auto drive system, laying the foundation for a more intelligent and efficient transportation system.
   4、 The advantages of pure visual intelligent driving solutions
   （1） Cost advantage
7. Hardware cost reduction: The pure visual intelligent driving solution reduces the use of hardware such as millimeter wave radar and LiDAR, resulting in a significant reduction in the overall hardware cost of the vehicle. Lidar, as a high-precision sensor, has a high cost, with the cost of one lidar possibly reaching thousands of dollars or even higher. The pure visual solution mainly relies on cameras, which have relatively low costs. The price of a high-definition camera may only be a few hundred dollars. This enables vehicle manufacturers to save a significant amount of costs in the production process, thereby reducing the selling price of vehicles and improving the market competitiveness of their products.
8. Cost reduction of system integration: Due to the fewer types and quantities of sensors required for pure visual solutions, the difficulty of system integration is correspondingly reduced. No need for complex calibration and fusion processing of multiple sensors, reducing the workload in the research and production process and lowering the cost of system integration. At the same time, the camera has a smaller volume and is more flexible to install, which will not have a significant impact on the design and aerodynamic performance of the vehicle, further reducing the design and manufacturing costs of the vehicle.
   （2） Strong ecological adaptability
9. Single data channel: The intelligent driving system software and hardware of domestic car companies usually adopt a three party+self-developed, radar+camera mode, which may lead to compatibility issues. And pure visual systems only require one data channel for data modeling, making post optimization and upgrading more singular. This means that software developers can focus more on processing camera data and optimizing algorithms, without having to consider the fusion and compatibility issues of multiple sensor data, improving development efficiency and system stability.
10. Easy to integrate with other systems: Pure visual intelligent driving solutions can be more easily integrated with other systems of the vehicle, such as in car entertainment systems, navigation systems, etc. By deeply integrating intelligent driving functions with other systems, users can be provided with a more convenient and intelligent travel experience. For example, users can view the real-time operation status of the intelligent driving system through the in car entertainment system, or share navigation information with the intelligent driving system to achieve more accurate navigation and path planning.
    （3） The algorithm has high scalability
11. Data driven deep learning mode: The pure visual intelligent driving solution is based on the data-driven deep learning mode, and the visual perception system can continuously iterate and optimize to adapt to more complex scenarios and long tail problems. Through large-scale data collection and training, visual algorithms can quickly adapt to different weather conditions, road conditions, and rare traffic scenes. For example, when facing new traffic signs, road construction, and other situations, algorithms can continuously optimize their recognition and decision-making abilities by learning new data, improving the adaptability and robustness of the system.
12. Rapid adaptation to technological updates: Compared with multi-sensor fusion solutions, the algorithm updates of pure visual solutions are more flexible and fast. Multi sensor fusion schemes often require individual optimization for each sensor, and the development cycle is relatively long. Pure visual solutions only require updating and optimizing visual algorithms to achieve functional improvements and enhancements. This enables pure visual intelligent driving solutions to adapt more quickly to technological developments and market demands, maintaining a leading position in technology.