Author | Zhang Lianyi, Editor | Jiang Tianxiang
Rino.ai has raised more funding.
On October 8, this L4 autonomous delivery company announced the completion of its Series C2 funding round, bringing the cumulative amount of its Series C to $100 million. This round was led by Hidden Hill Capital, with follow-on investments from Xiangtan State-owned Capital, Hunan Caixin, Woori Financial Group of Korea, and Bozheng Capital.
It has been only about five months since Rino.ai announced the completion of its Series C1 funding round in May this year.
Founded in 2019, Rino.ai is an L4 autonomous driving solution provider. Starting with autonomous delivery, it primarily focuses on autonomous driving products and services for urban public roads.
As a startup, what enables it to survive in a track characterized by ruthless technological iteration and surrounded by industry giants? What challenges will it need to overcome in the future?
01. Who is Funding and Where the Money Goes
Let's start with this funding round. The highlight is not the amount itself—$100 million is not an astronomical figure in the current autonomous delivery track. What is more worth looking at is the "composition" of the investors.
Hidden Hill Capital is a private equity investment platform under GLP, with assets under management exceeding CNY 30 billion. It has long focused on logistics technology and digital-intelligent supply chains, having invested in J&T Express, Kyexpress, and G7 Connect. As a global giant in logistics real estate, GLP holds extensive warehousing parks and logistics infrastructure across various regions. For Hidden Hill Capital to lead the investment in Rino.ai, the rich application scenarios behind it are even more valuable than the money itself.
The addition of Xiangtan State-owned Capital and Hunan Caixin directly echoes Rino.ai's previous signing with Xiangtan to establish a headquarters base for intelligent logistics vehicles.
Woori Financial Group is the first overseas capital to appear on Rino.ai's shareholder list. Woori is a representative financial group in South Korea with business outlets in multiple countries worldwide. Rino.ai's press release explicitly mentioned the intention to "rely on the cross-border resources of shareholders such as Woori Financial Group" to expand overseas. With the entry of South Korean capital, the overseas expansion path now has its first springboard.
After raising the funds, where will the money be spent?
The official statement is: iteration of L4 end-to-end technology and physical AI capabilities, expansion of the urban operation network, and industrial ecosystem synergy.
Regarding physical AI, according to previous public information, Rino.ai's "physical AI" direction extends the capability boundary from autonomous driving to embodied intelligence, with the goal of making the vehicles not just drive, but also work. The dual-cage dump truck jointly developed by Rino.ai and SF Express has been put into operation in SF Express's transit scenarios, capable of automatically loading and unloading transfer cages. In other words, the vehicles are no longer just mobile carriers, but are evolving into "mobile robots with integrated operation capabilities from loading/unloading to transportation."
If automotive-grade standards solve the problem of "whether the vehicle is built well," physical AI solves the problem of "what the vehicle can do." The leap from a transportation tool to an operational robot is perhaps the story this funding round aims to tell.
The signal for horizontal expansion of scenarios is also very clear. In April this year, Rino.ai and Lalamove launched large-scale autonomous freight operations in Linyi, Shandong. The automotive-grade logistics vehicle RX was integrated into the Lalamove platform, providing on-demand autonomous delivery for C-end users and small/micro merchants. The fleet rapidly expanded to 200 units, operating on a normalized 24/7 basis. In Weifang, Rino.ai partnered with SF Intra-city to integrate high-timeliness on-demand delivery scenarios such as supermarkets, fresh produce, and retail.
These scenarios fill the blank market between "using errand runners for small parcels and trucks for large goods." Reportedly, Rino.ai's autonomous vehicles can carry a volume of 6 cubic meters and a weight of over 1 ton. For merchants with fixed delivery needs, the cost per order can be reduced by about 50%.
02. Emerging from Baidu and Taking Root in Express Logistics Scenarios
The story of Rino.ai began at Baidu. Founders Zhu Lei and Xia Tian both came from Baidu's autonomous driving team. Zhu Lei has over a decade of experience in the autonomous driving industry and is among the earliest entrepreneurs in the domestic unmanned logistics vehicle sector; Xia Tian is a founding member of Baidu's Institute of Deep Learning.
At the beginning of the company's establishment in 2019, Rino.ai started with supermarket delivery, cooperating with retail enterprises such as Yonghui Superstores and Freshippo to conduct autonomous delivery for fresh food supermarkets in Beijing and Shanghai. With fixed routes, real demand, and rapid technological validation, this was the easiest business story to explain clearly at the time.
However, the ceiling for supermarket delivery is not very high. The turning point occurred in 2023.
In this year, Rino.ai shifted its focus to the express logistics scenario, concentrating on last-mile delivery from "network nodes to delivery stations." This was a crucial strategic judgment: last-mile express delivery features fixed routes, extremely high frequency, and a large proportion of labor costs. Moreover, the clients are giants like SF Express, ZTO Express, and China Post. Once the model is proven, economies of scale will rapidly emerge.
Looking back, this shift defined Rino.ai's growth curve for the subsequent three years.
Before the strategic shift, Rino.ai's investors were mainly industrial capital, including Linear Capital, Cornerstone Capital, Chentao Capital, and Yunjin Capital.
After the transformation, Rino.ai finally attracted SF Express. In August 2024, SF Express made its first investment. Within the following year, SF Express added investments multiple times. In the Series B+ funding round in August 2025, SF Express followed up with another investment, bringing the cumulative total of Series B funding to nearly CNY 500 million.
It is not very common in the industry for a logistics giant to invest so intensively in the same startup. SF Express's logic is not hard to understand: last-mile delivery is the highest and most difficult-to-compress part of the cost structure in the express delivery industry. Couriers' round trips between network nodes and delivery stations take up 4 to 5 hours every day. If this part of the transport capacity can be replaced by autonomous vehicles, the freed-up manpower can focus on pickup and delivery, making the room for business volume growth directly visible.
SF Express's continuous investment also sends a signal: Rino.ai's technology is at least usable. Logistics giants will not pay for "concepts"; only real operational data can convince them to write checks time and time again.
03. Delivering 5,000 Vehicles and Connecting Three Major Links
The premise for capital willing to place heavy bets is that the technology and products have been validated.
According to the latest data disclosed by Rino.ai, it has established deep cooperation with logistics and retail leaders such as SF Express, China Post, JD.com, SF Intra-city, Lalamove, and Taobao Flash Purchase. It has cumulatively delivered over 5,000 unmanned logistics vehicles. Its business covers nine major application scenarios, including express delivery and intra-city freight, and operates on a normalized basis in over 200 cities nationwide.
Rino.ai claims that on the track of L4 autonomous driving, it has connected three major links: algorithms, product definition, and transport capacity delivery.
At the algorithm level, the main lines are map-free solutions and physical AI. The map-free solution breaks free from the reliance on high-definition maps and can be deployed across the city using ordinary navigation maps. In 2026, Rino.ai RX's map-free solution has been implemented and operated in Shandong and other regions, reducing per-vehicle costs and accelerating the pace of urban deployment; physical AI extends the capability boundary from autonomous driving to embodied intelligence.
At the product level, it features a complete matrix covering all urban distribution scenarios. Rino.ai RX is a fully automotive-grade RoboVan: developed in 18 months, with a design life of 8 years or 300,000 kilometers, 100% automotive-grade components, and thousands of units delivered since its launch in April 2026. Rino.ai R24 is a 24-cubic-meter large-volume light truck autonomous vehicle, benchmarked against small and medium-sized box trucks. It has been successively delivered to customers for operation and is undergoing public road testing in some cities, filling the market gap for large-item transfer in parks, warehouse-to-store allocation, and short-distance high-capacity urban distribution.
At the commercial level, it follows a dual-wheel path of "technology output + transport capacity services." Rino Inside integrates the full-stack software stack into a standardized L4 integrated hardware-software solution, opening up licensing to OEMs; Rino FaaS (Fleet as a Service) directly delivers transport capacity to customers, who settle based on actual waybills. During the trough of express delivery business, vehicles can be transferred to intra-city freight scenarios, steadily increasing the utilization rate of single-vehicle assets.
Algorithms determine whether they can enter the market, product definition determines whether they can be mass-produced, and transport capacity delivery determines whether they can generate their own cash flow. The three layers of capability are indispensable.
04. Real Challenges at the Tipping Point
Reality is not just about growth; there are also difficulties.
Huang Gang, President of Rino.ai, systematically elaborated on the challenges facing the industry at an industry forum in 2026. His judgment can be summarized in four aspects: technology is not a panacea, vehicle platforms need to prove themselves, lack of road right standards, and the premature arrival of a price war.
The challenges at the technological level are the most fundamental. Xia Tian put it more bluntly in a previous interview with "Cyber Auto": currently, there are only tens of thousands of autonomous delivery vehicles nationwide, and the density is very low when dispersed across various cities. "If there is an occasional stutter or a minor problem on the road, the public and regulatory departments can still tolerate it." But if the scale expands to hundreds of thousands of units, the tolerance will drop sharply. "We can still see autonomous vehicles exhibiting behaviors that do not conform to human driving habits. These must be resolved before a larger scale arrives."
This is not modesty. The "iPhone moment" for autonomous delivery requires two conditions to be met simultaneously: at the technological level, running through the complete L4 end-to-end process and establishing an efficient data growth methodology supported by AI infrastructure; at the operational level, achieving full-process automation of vehicle scheduling and autonomous receipt and dispatch of goods. Xia Tian's judgment is that this state is still two to three years away.
The greater structural dilemma lies in road rights. Currently, there are no nationally unified mass production testing standards and road right management rules for autonomous delivery vehicles in China. Each city conducts independent certification, and the cost for enterprises to achieve large-scale cross-regional implementation remains high. Huang Gang frankly stated that this brings a dual impact on efficiency and cost for the further expansion and market promotion of autonomous vehicles.
Meanwhile, just as the industry has reached a growth inflection point, a price war has already broken out. "Just as the market in this industry has reached a growth inflection point, it faces a highly competitive and intense situation," Huang Gang said. For a startup that needs continuous R&D investment and has not yet achieved profitability at scale, premature price competition is a drain.
Compared to the other two leading enterprises in the autonomous delivery field, Neolix and Jiushi, Rino.ai has no advantage in either the number of vehicles or the scale of financing.
The business model is equally challenging. Pay-per-order is the transport capacity service model mainly promoted by Rino.ai, and the cost pressures brought by vehicle operation, maintenance, and idleness are borne by the enterprise itself. In addition, after express delivery and intra-city freight are stuffed into the same scheduling system, whether the effective operation time of the vehicles can truly be pulled up still requires daily data to corroborate.
"The fact that autonomous delivery is a business is becoming more and more established." This is a quote from Xia Tian, and perhaps it is the best footnote for this stage.
The establishment of the business means that the demand is real, the math works out, and customers are willing to pay continuously. But between "the establishment of the business" and "large-scale commercial success," there are still multiple hurdles: the long tail of technology, the lack of standards, the game of road rights, and price competition.
$100 million is a considerable amount of ammunition, but it is far from enough. On this track, all players are racing against time: to complete the leap from "thousands of units" to "tens of thousands of units" before the price war completely destroys the profit margin, before the regulatory framework tightens, and before competitors establish irreversible scale barriers.
Rino.ai has taken seven years to reach this tipping point. The road ahead tests not only the ability to raise funds but also the ability to transform capital into sustainable operational efficiency.