In the technological routes of autonomous driving, there has been considerable debate between pure vision and LiDAR fusion, with differing perspectives even at the algorithmic level. Many believe that LiDAR adds perceptual dimensions, thereby reducing algorithmic complexity and requiring less computing power. Others argue that LiDAR introduces massive amounts of 3D data, which actually demands greater computational capability from the system. In fact, both views hold some merit but fail to touch the core issue. The computing power requirements of an autonomous driving system are not simply determined by the number of sensors, but depend on the data processing methods of the perception scheme, algorithmic complexity, model scale, and the depth of the system's environmental understanding. Judging from current technological development trends, the pure vision route relies more heavily on large models and intensive computing. Although the fusion scheme dominated by LiDAR increases data volume, it can alleviate some algorithmic pressure during the perception stage. In the future, the difference in computing power requirements between the two routes may not be reflected in who processes more data, but rather in who requires more sophisticated intelligence.
01. LiDAR Generates More Data but with More Direct Processing Logic
The greatest advantage of LiDAR is its ability to directly acquire spatial information. Traditional cameras can only capture 2D images, requiring vehicles to use deep learning algorithms to infer the position, distance, and 3D structure of objects from pixels. This is essentially a process of inferring the 3D world from 2D information. LiDAR, on the other hand, emits laser beams and receives reflected signals to directly generate point cloud data, with each point containing spatial coordinate information. Therefore, when identifying vehicles, pedestrians, and obstacle boundaries, the information provided by LiDAR is much closer to the physical world description truly needed by autonomous driving systems.
However, this does not mean that the LiDAR scheme does not require high computing power. LiDAR generates massive amounts of discrete point cloud data; a high-performance LiDAR can output hundreds of thousands or even more point cloud data points per second. The system needs to filter, cluster, detect, and track these point clouds, and fuse them with data from sensors such as cameras and millimeter-wave radars. Especially in multi-sensor fusion schemes, the computing platform must process different types of data simultaneously. Cameras output high-resolution images; LiDAR outputs 3D point clouds; millimeter-wave radars output velocity and distance information. Temporal synchronization, spatial calibration, and feature fusion of these data all require additional computing resources. Therefore, the LiDAR route does not simply add a sensor but introduces a complex data processing pipeline. Nevertheless, in terms of algorithmic difficulty, LiDAR indeed alleviates some pressure. For example, when identifying an irregularly shaped obstacle by the roadside, a pure vision system needs to learn what the object is through massive training data and then predict the spatial range it might occupy. LiDAR, however, can directly perceive the spatial contour of the object. Even if it cannot accurately identify what the object is, it can recognize the presence of an impassable area ahead. This is also a crucial reason why the LiDAR scheme holds an advantage in complex environments.
02. Why Pure Vision Increasingly Relies on Massive Computing Power?
In the past, autonomous driving primarily relied on modular architectures. After cameras captured images, multiple modules such as object detection, lane line recognition, and obstacle prediction were used to achieve environmental understanding. The problem with this approach is that each module requires individually designed rules and trained models. When faced with complex road environments, the system is highly susceptible to encountering edge cases. In recent years, new architectures represented by end-to-end large models have begun to change this paradigm. End-to-end autonomous driving no longer tackles tasks like vehicle recognition, lane judgment, and route planning separately. Instead, it utilizes large-scale neural networks to enable the system to directly output driving decisions from input visual data. This approach is closer to the human driving process, but the trade-off is a substantial increase in model scale. Because cameras themselves do not provide direct 3D information, the system must be trained on massive amounts of data to enable the model to learn spatial relationships, traffic rules, and vehicle motion patterns.
When facing a plastic bag by the roadside, the LiDAR scheme might first determine the presence of a low-height obstacle and then combine visual data to decide whether to avoid it. A pure vision system, however, needs to understand the object's shape, material, and motion state from the image, and determine whether it will affect vehicle driving. This requires stronger semantic understanding capabilities behind the scenes. Therefore, the pure vision route imposes higher requirements on AI models. This is why some pure vision autonomous driving systems have started to introduce larger neural networks, longer video inputs, and more complex training systems in recent years. It is not merely about improving image recognition capabilities, but about enabling the vehicle to build a world-model-like understanding capability. From this perspective, the advantage of pure vision is not lower costs, but rather shifting some tasks originally handled by sensors to algorithms and computing power.
03. The Core of Computing Power Competition is Shifting from Data Processing to Intelligent Understanding
If we only look at the perception stage, the LiDAR fusion scheme may not consume more computing power than pure vision. This is because LiDAR provides explicit spatial information, which can significantly reduce visual reasoning tasks. However, if we look at the future development direction of autonomous driving, the situation might be different. Future vehicles will need to process not just what they see, but understand what is happening. If a pedestrian is standing by the roadside, will they suddenly step onto the road in the next second? If a car is stopped at an intersection, is it a temporary stop or preparing to change lanes? If an abnormal situation occurs on the road ahead, what strategy should the vehicle adopt? These are all issues that require flexible responses from autonomous vehicles.
These issues have gone beyond simple perception and entered the realm of prediction and decision-making. At this stage, the system needs to understand causal relationships in the environment and utilize larger-scale data to train models. This is also the reason why the industry is continuously exploring vision large models, world models, and VLA (Vision-Language-Action models). In the future, regardless of whether LiDAR is equipped, autonomous driving systems will increasingly rely on large model capabilities. LiDAR can enhance perception reliability but cannot replace intelligent reasoning. Pure vision can reduce hardware costs but requires stronger data and algorithmic capabilities. The ultimate focus of competition between the two will no longer be just the number of sensors, but who can understand complex road environments in a more efficient manner.
04. Concluding Remarks
From the perspective of technological development, there is no absolute superiority or inferiority between pure vision and LiDAR fusion. The advantage of the pure vision route lies in its easily scalable data volume, simpler cost structure, and better alignment with the development direction of AI models. However, it imposes higher requirements on training data, model capabilities, and computing platforms. The advantage of the LiDAR fusion scheme lies in its stronger physical perception capabilities, which can reduce the difficulty of environmental understanding to some extent. However, it needs to address issues such as sensor costs, data fusion complexity, and systems engineering. In fact, as algorithmic capabilities improve, vehicles will gradually reduce their reliance on rule-based perception, turning sensors into mere information entry points. What truly determines autonomous driving capabilities are the underlying computing architectures and intelligent models. From this perspective, the pure vision route is more like trading computing power for sensors, while the LiDAR route is more like using sensors to alleviate some computing power pressure.