The accelerated implementation of Autonomous driving: An Analysis of Three Core Elements

On the stage of the 2025 International Consumer Electronics Show (CES 2025),AI is like a brilliant new star, achieving comprehensive applications in the fields of intelligent cockpits, vehicle control, and especially in autonomous driving.This breakthrough progress has brought about a disruptive transformation to the entire industry, demonstrating a significant recovery trend in the autonomous driving sector and resolutely moving towards a critical turning point of technological explosion and commercialization.

From the perspective of professional technology, a thorough analysis reveals that the popularization of autonomous driving cannot be achieved without the strong support of three core elements.Firstly, it is the leading algorithm,It is like the intelligent brain of autonomous driving, dominating the vehicle's decisions and behaviors;Secondly, there is sufficient computing power, which can be regarded as the driving force of autonomous driving, providing a solid guarantee for the efficient operation of algorithms;Finally, there is the abundant data, just like the knowledge reserve of autonomous driving, enabling the system to continuously learn and optimize.

At present, these three major conditions have been initially met, laying a solid foundation for the development of autonomous driving.

Two years ago, in the market promotion of autonomous driving chips, the main focus was on the comparison of parameters such as AI computing power, power consumption, and manufacturing processes.However, the computing power of today's main control chips has achieved an astonishing leap, significantly increasing from single-digit TOPS (trillion floating-point operations per second) to several hundred or even thousands of TOPS.Industry experts predict that as the pace of computing power improvement continues to accelerate and hardware costs gradually decline, it is highly likely that a brand-new scenario will emerge in the future where computing power can be enhanced by replacing computing modules.This trend will not only drive the continuous upgrade of computing power but also bring more possibilities to the development of autonomous driving.

In terms of engineering capabilities, after years of fierce competition and continuous training,Leading autonomous driving companies have generally mastered the core capabilities to transform advanced technologies into practical products.By accumulating and "feeding" trillions of kilometers of massive data, a series of all-domain, all-time, low-cost, mass-producible and automotive-grade standard autonomous driving solutions have emerged like mushrooms after rain.The emergence of these solutions marks a major breakthrough in the engineering practice of the autonomous driving industry, providing strong support for the commercial application of autonomous driving.

In terms of algorithms, deep learning algorithms are reshaping the development patterns of all industries with their powerful influence, and this is especially true for the autonomous driving industry.With the rise of multimodal large models, including the emergence of new algorithm frameworks such as "End-to-end 2.0" VLA (Visual Language Action Model), continuous performance improvements have been demonstrated at the boundaries of data volume, computing resources, and model complexity. These new algorithm frameworks have significant advantages, which can greatly reduce repetitive data and computing resources, while lowering model complexity, truly promoting the magnificent leap of intelligent driving technology from quantitative change to qualitative change.

Overall, autonomous driving is a complex system research and development project that requires a solid scientific research and engineering team to maintain a persistent attitude and continuously accumulate valuable experience in practice. At the same time, the team must also possess acute insight, promptly absorb the latest technologies, and deeply understand the boundaries of technology. Only in this way can technological innovation step by step move towards reality, promote the continuous development of the autonomous driving industry, and achieve large-scale commercial application as soon as possible.