May. 08, 2025
Source: 36Kr
36Kr Auto has learned exclusively that Rino.ai, a leading low-speed autonomous driving company, has recently completed a RMB 200 million Series B financing round. This round was led by SF Express, with participation from Xinyuan Auto and existing investor Linear Capital.
The funding will be primarily used for the development of new low-speed autonomous vehicle products and for the marketing and promotion of existing products.
Founded in April 2019 by former members of Baidu’s autonomous driving team, Zhu Lei and Dr. Xia Tian, Rino.ai develops full-stack unmanned delivery solutions and provides normalized operations for autonomous vehicles. In March of this year, Rino.ai announced that former General Manager of Dongfeng Commercial Vehicles, Huang Gang, had joined the company as President.
Logistics as the Core Landing Scenario for Low-Speed Autonomous Driving
Low-speed autonomous driving has been one of the earliest segments in the autonomous driving industry to achieve commercialization. CEO Zhu Lei observed that in previous years, startups in this sector pursued a diverse range of use cases, such as campus delivery, unmanned retail, and community group buying.
By early 2024, however, the business model became clearer:
“The main landing scenarios have converged on the logistics sector, particularly the segment from courier distribution centers to parcel pickup stations,” Zhu said.
In this niche, the round-trip distance per order typically ranges from 10–30 kilometers. Currently, 80–90% of all unmanned delivery in the industry takes place in this scenario, and it is expected to remain the primary driver of large-scale deployment over the next 1–2 years.
Other startups such as Neolix and Idriverplus, as well as large-scale operators like JD Logistics, Cainiao, and Meituan, are also investing heavily in this space. One key metric for evaluating commercialization progress is the number of autonomous vehicles deployed in operation for a specific scenario.
However, according to Rino.ai, production and sales volume alone do not accurately reflect the efficiency gains from the technology. Instead, daily active vehicles — the number of AVs actually operating in deliveries each day — is a more critical indicator.
“Autonomous delivery vehicles are essentially productivity tools. A vehicle running only two trips a day delivers very different value compared to one running ten trips. But reaching ten trips a day requires reengineering existing logistics processes to fully integrate AVs — and that’s a gradual process,” Zhu explained.
Deep Integration with SF Express and Large-Scale Daily Operations
Since Q2 2024, some SF Express courier outlets have begun deploying Rino.ai’s autonomous vehicles. The company now operates several hundred daily active AVs within SF Express’ logistics network.
Business models in the low-speed AV industry vary, including vehicle sales, subscription fees, and delivery capacity services. Rino.ai believes that the truly stable and mature commercial model for autonomous freight delivery still needs further market validation.
Rino.ai’s competitive advantage lies in its deep partnerships with major logistics and retail players, including SF Express, STO, YTO, Yonghui Superstores, Dada Express, Freshippo, and Ele.me. These collaborations provide a wide range of operational scenarios to continuously validate the feasibility of its technology and products. The company aims to reach 5,000 daily active delivery vehicles by 2026.
Technology Evolution and Product Advancements
Rino.ai’s flagship product is the R5 series autonomous vehicle, featuring a 5.5 m³ cargo compartment capable of carrying over 500 parcels, with a fully loaded range of more than 120 km per charge.
Zhu notes that the low-speed AV segment benefits greatly from advancements in passenger car driver-assistance technologies. Current deployment requires AVs to safely operate at 30–40 km/h on public roads, which demands advanced AI capabilities.
According to official information, Rino.ai has integrated large-model AI technologies into its AV system training. Even in challenging conditions — such as rain, fog, and urban rush hours — the vehicles can handle complex tasks like traffic light recognition and construction site detours.
The recent boom in passenger car ADAS has also accelerated the maturity and cost reduction of the autonomous driving supply chain:
“Automotive-grade components like LiDAR and computing platforms have improved significantly in performance and price,” Zhu said.
With President Huang Gang now in charge of large-scale mass production, product planning, new vehicle development, manufacturing, supply chain management, and industry partnerships, Rino.ai aims to meet full automotive-grade standards and be ready to capture the market when the commercial inflection point for autonomous vehicles arrives.
