The advanced driving assistance industry is currently at an awkward tipping point.
The monthly report on advanced driving assistance released by Gasgoo Research Institute shows that in August 2026, the installation volume of L2 and above advanced driving assistance systems in new domestic vehicles reached 1.1 million units, with a penetration rate of 73.6%. Among them, the monthly installation volume of L2++ level models (including highway NOA and urban NOA) reached 545,000 units. High-level advanced driving assistance is gradually becoming a standard configuration.
However, installation does not equate to effective utilization. A user survey conducted by Gasgoo Research Institute paints a different picture: users remain generally unfamiliar with and uninformed about new intelligent driving features. Safety, handling of unexpected situations, and system errors continue to be the core concerns for consumers regarding advanced driving assistance systems.
In other words, the features installed at high costs may remain in "sleep mode" most of the time. The hardware has been deployed, but trust has not kept pace.
It is precisely at this turning point that the Huawei Qiankun National Day Holiday User Travel Report takes on a methodological significance that transcends mere corporate behavior. While most companies' data disclosure remains at the stage of highlighting single positive metrics, Huawei Qiankun has chosen a "heavier" path: sufficiently fine data granularity, sufficiently authentic scenario coverage, and sufficiently transparent public metrics, allowing users to make their own judgments based on facts rather than promises.
The Dimensional Reduction Strike of Huawei Qiankun's Data Disclosure
Recently, many automotive brands have published data on their respective advanced driving assistance mileage, usage rate, and activation rate. The direction is consistent: with the rapid increase in the penetration rate of advanced driving assistance systems, continuing to talk about installation volume is no longer enough to impress end users. Whether the advanced driving assistance system is easy to use after purchasing the car, and the universal, real usage situations of car owners, are the issues more worthy of attention.
But how exactly should the extent of usage be measured? In fact, each company has its own algorithms, and the data calibers are not unified. What users face is no longer which advanced driving assistance system is better to use, but rather, who should they trust?
The root of this confusion lies in the choice of disclosure metrics by each company.
Observing the disclosure metrics of various companies reveals a commonality: cumulative mileage is the most frequently cited indicator. Claims such as the total advanced driving assistance mileage doubling, or the longest advanced driving assistance mileage of a single vehicle reaching nearly 10,000 kilometers, are sufficiently grand and "good-looking," but difficult to verify.
Huawei Qiankun also published another set of metrics, starting with the proportion of active users. During the 7-day National Day holiday, the number of active users of advanced driving assistance reached 1.5972 million, accounting for 90.17%. This is not the activation rate of pre-installed features, nor the conversion rate of a single trial use, but the real proportion of users who actively activate and use advanced driving assistance in the most complex travel scenarios of the year.
The authenticity of safety is a more weighty footnote to this set of usage data: during the holiday, 58,200 potential collisions were actively avoided, defensive driving was triggered 14,500 times under manual driving conditions, fatigue detection reminders were issued 20.92 million times, and distraction detection reminders were issued 54.75 million times. These safety interventions, measured in "times," correspond to one imminent risk moment after another on real roads. This is data that cannot be generated in a laboratory; it can only come from every intervention during high-frequency real-world usage.
There is an easily overlooked logical chain here: the high-frequency occurrence of safety intervention data precisely indicates that users are genuinely using the system, rather than just trying it out in the short term. Only when the system is in a long-term active state can it continuously capture those imminent risk moments.
Safety data and usage data corroborate each other, jointly pointing to a fact: Huawei Qiankun has been integrated into users' daily driving behaviors.
Scale Significance: Not Just a Sales Milestone, but the "Tipping Point" of the Data Closed Loop
To understand the strategic implications of the Huawei Qiankun National Day Travel Report, it is also necessary to place it back into the scale coordinate system for examination.
In August 2026, the installation volume of Huawei Qiankun Intelligent Driving surpassed 2 million units. Compared to the 44 months it took to achieve the first 1 million units, it took only about 12 months to complete the second 1 million units, showing a significant increase in growth rate. Currently, Huawei Qiankun has cooperated with 25 vehicle brands and over 60 vehicle models, covering a wide range from mainstream price segments to flagship models.
Of course, the significance of reaching a scale of 2 million units goes far beyond sales. Gasgoo Research Institute's analysis points out that the algorithm iteration of advanced driving assistance systems is essentially highly dependent on the continuous feeding of real-road data. If the scale is too small and the data pool is not deep enough, algorithm iteration will be slow; if the scale is large enough, the real driving scenarios generated by users every day can better cover the vast majority of long-tail cases, and the algorithm iteration speed will increase exponentially.
An installation scale of 2 million units can better support the high-speed operation of the iteration cycle.
In the operational logic of this cycle, scale generates data, data feeds back into algorithms, algorithms improve the experience, the experience drives more users to actively use the system, and the growth in usage further expands the data pool. In this data flywheel, every time a user activates advanced driving assistance and every safety intervention accumulates training materials for the next-generation algorithm.
Data published in the Huawei Qiankun National Day Travel Report shows that the advanced driving assistance mileage of the ADS Max V5.0 version accounts for 60.8%, which is the best testament to the continuous acceleration of this flywheel. Users are not using it passively, but actively migrating to the new version.
Supporting the acceleration of the flywheel are also the generational leap of underlying hardware and the continuous evolution of the architecture. The technical foundation of Huawei Qiankun Intelligent Driving ADS 5 is the "smart brain" and "bright eyes" of Huawei Qiankun Intelligent Driving.
The "smart brain" of ADS is first reflected in its exceptional computing power, possessing 60 EFLOPS of cloud computing power, ranking first in China; it is also reflected in its mature thinking, the WEWA 2.0 world model, and industry-leading algorithms; and even more in its rich experience, with advanced driving assistance mileage exceeding 16.5 billion kilometers, ranking first in China for real user mileage data. Computing power provides training efficiency, algorithms provide thinking frameworks, and data provides training materials. The superposition of the three enables ADS to possess deep and meticulous thinking and rapid on-the-spot reactions, and it can continuously evolve, learn new safety capabilities, and become smarter and smarter.
The "bright eyes" of ADS represent the multi-sensor fusion perception route that Huawei Qiankun has consistently adhered to. In addition to high-definition cameras, the industry's highest 896-line dual-optical-path image-grade LiDAR, the industry's first high-precision solid-state LiDAR, and distributed millimeter-wave radars jointly form a hardware combination that can maintain stable perception even in extreme scenarios, safeguarding travel safety.
A smart "brain" combined with a pair of bright "eyes" pushes up the efficiency of accurate judgment at the decision-making end and extends the boundary of clear perception at the perception end. The two together constitute the technical foundation for the continuous acceleration of the data flywheel. Once the flywheel operates at high speed, the difficulty for latecomers to catch up is no longer single-point technology, but a composite barrier composed of "data scale + iteration speed." This is also the best annotation of Huawei Qiankun's positioning as an "electronic screw," not manufacturing cars but only acting as an incremental component supplier, allowing the popularization of advanced driving assistance to no longer rely on the sales of a single brand, but to be jointly promoted by the entire ecosystem.
For users, this means they can buy a more mature intelligent driving experience at the same price point; for the industry, it means the pace of popularization of advanced driving assistance is determined by the combined efforts of the entire ecosystem, rather than the success or failure of a single automaker.
Conclusion: Turning "Trust" into a Verifiable Public Standard
The domestic advanced driving assistance industry is undergoing a fundamental shift in competitive logic.
In the past few years, the main theme of the industry has been to prove its capabilities, with core goals such as how high the computing power is, how strong the sensors are, and the number of cities opened for use. However, with the rapid increase in market penetration, the utility of this narrative is gradually diminishing. What users face is no longer the question of whether they have advanced driving assistance, but whether they dare to use it.
The reference system for competition in sub-sectors has shifted from who speaks more eloquently to who uses it more and uses it better. Behind this turning point is a profound change in the industry from technology-driven to trust-driven.
The significance of the Huawei Qiankun National Day Travel Report lies precisely in turning the originally vague concept of trust into a set of traceable and cross-verifiable public data. This is also Huawei Qiankun's most essential contribution to the industry. When the disclosed information can be verified, the trust cost of the industry will decrease. Moreover, data transparency, transforming from a conscious behavior of a single enterprise into an industry standard, will inevitably accelerate the advanced driving assistance system in completing the leap from technical credibility to social credibility.