Mining Autonomous Driving: The Optimal Path for Commercializing Closed-Loop Scenarios, with Overseas Markets Opening a New Growth Curve

Stock News15:08

China Securities Co., Ltd. has released a research report stating that mining autonomous driving differs from Robotaxi, with closed-loop scenarios, rigid labor demand, and clear cost-reduction and efficiency-improvement benefits making its commercialization path more defined. After the penetration rate exceeds 10%, the industry is expected to enter an S-curve acceleration phase. With nearly 100,000 mining trucks in China offering ongoing retrofitting potential, the expansion of large coal mine fleets, replication of metal mining projects, and penetration into small and medium-sized mines will support domestic market growth. Overseas, markets like Australia, with high labor costs, offer significantly higher per-vehicle service value than domestic markets, potentially opening a second growth curve. Key views from China Securities Co., Ltd. include:

Policy, labor, and efficiency are the three drivers, with mining leading L4 commercialization. Compared to urban Robotaxi, mining autonomous driving has clearer regulatory frameworks, more deterministic technology paths, and primarily addresses issues like hard-to-fill high-risk positions and high ongoing operational costs, leading to higher commercial acceptance. Policy-wise, there is a push for mine automation and robot replacement in dangerous jobs; economically, autonomous driving can reduce per-vehicle labor costs by about 180,000 yuan per year, offering clear cost-saving potential; operationally, unmanned mining trucks can operate 24/7, enhancing vehicle utilization; and safety-wise, reducing personnel entry into high-risk transport areas lowers accident risks.

Industry chain collaboration and domestic and international demand synergies drive unmanned mining trucks into a scale-up phase. With nearly 100,000 mining trucks in China and a low penetration rate, coal mines are the primary application scenario, while metal and large-scale sand and gravel mines are expected to contribute future growth. As the penetration rate exceeds 10%, the domestic unmanned mining truck scale is projected to reach 20,000 units by 2030, with a corresponding technology service market of about 4 billion yuan and an overall market space exceeding 5 billion yuan. Overseas, large mining areas in Australia, with high labor costs and mature automation bases, can see per-vehicle annual service value reaching 2 million yuan, while resource-driven markets like Central Asia, Indonesia, and South America offer scalable export opportunities for domestic solution providers.

Yi Kong's light-asset replication accelerates, driven by technology and overseas expansion. The company's 2025 revenue reached 1.435 billion yuan, with 2,580 active unmanned mining trucks, and the proportion of customer-provided fleet model revenue rose to 56.8%, indicating a shift from heavy to light models. Relying on the "Zhushan + Muye + Yushi" full-stack technology system, it covers over 70 vehicle models and operates across 30-plus mining areas, building a data closed-loop that strengthens cross-vehicle and cross-mine replication capabilities. Domestic large-scale mine expansion and small-to-medium mine penetration provide sustained growth, while Australian projects have entered real production, with per-vehicle annual service value potentially reaching 1-2 million yuan, making high-value overseas markets a second growth curve.

Risk analysis includes: slower-than-expected penetration of mining autonomous driving, slower-than-expected promotion of light-asset business models, high concentration of domestic large-scale mine customers, mismatched technology iteration and complex scenario adaptation, increased industry competition leading to profitability pressure, slower-than-expected overseas market expansion, lower-than-expected overseas high-value service fees, continuous R&D and overseas investment increases hindering profitability improvement, and risks from mine safety regulations and autonomous driving policy changes.

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