Rino.ai, an autonomous driving technology company, has closed its Series B+ round, bringing its total Series B fundraising to nearly RMB 500 million (USD 70 million). Existing investors SF Express and Linear Capital increased their commitments, while new backers including Yijun Capital, a fund managed by Qihoo 360, and Huatai Zijin Investment also participated.
The company will allocate the new capital to three areas: developing autonomous vehicles, advancing artificial intelligence capabilities, and expanding commercial applications. This marks SF Express’ third investment in Rino.ai since first backing the startup in August 2024.
Founded in April 2019 by former Baidu autonomous driving team members Zhu Lei and Xia Tian, Rino.ai develops full-stack unmanned delivery solutions and operates driverless vehicles at scale. In March, it appointed Huang Gang, former general manager of Dongfeng Commercial Vehicle, as president.
The unmanned logistics sector is at a turning point, propelled by advances in technology, falling hardware costs, and regulatory support. Wider adoption of assisted driving in passenger cars has driven down LiDAR (light detection and ranging) prices, while regulators have opened road access to unmanned delivery vehicles in 103 cities. Large-scale road tests and commercial operations are already demonstrating that driverless logistics fleets can lower costs and improve efficiency.
Despite a cooling investment climate, funding in this field remains active. This year, Neolix raised RMB 1 billion (USD 140 million) in a Series C+ round, while Zelos secured USD 100 million in a Series B3 round. Rino.ai’s latest financing is its second in 2025, underscoring investor confidence that the sector is approaching critical mass for profitability.
Unmanned logistics shows early scale
Rino.ai entered the market through supermarket delivery, working with Yonghui Superstores and Freshippo (more commonly referred to as Hema in China). Since 2023, it has expanded into logistics operations, focusing on shipments from sorting depots to pickup points.
Applications of autonomous driving in logistics generally fall into three categories:
- Trunk logistics covers long-haul intercity or highway freight spanning hundreds of kilometers, but faces safety risks and regulatory limits.
- Feeder logistics handles transport across medium distances between distribution centers and urban depots, where complex traffic and policy hurdles remain.
- Last-mile delivery involves short-range routes, usually under ten kilometers, from urban depots to pickup stations or customers.
Rino.ai is focused on last-mile delivery. These routes are relatively fixed and carry fewer technical risks, while regulators have been more open to granting road access. Traditionally, couriers covered these routes with small vans or tricycles, making four to five trips a day at high labor cost. This frequency makes the unit economics favorable for unmanned vehicles.
The company’s flagship R5 series vehicle has a cargo capacity of 5.5 cubic meters, can carry over 500 parcels, and runs more than 120 kilometers on a single charge. Its vehicles serve major logistics operators including SF Express, ZTO Express, J&T Express, and China Post, and are already deployed across 100 cities in China.
Usage has grown quickly. According to CEO Zhu Lei, Rino.ai had around 100 active vehicles in December 2023. That number doubled in the first quarter of 2025, and reached 500 in the second quarter. By August, its fleet was close to 1,000.
Rino.ai aims to have 5,000 active vehicles by 2026. According to Zhu, with fewer than 10,000 driverless vehicles currently in operation, the market is still in an early “emotional consumption” stage, where customers are drawn to low-cost, intelligent products.
Huang noted that last-mile delivery is a bottleneck for couriers. “A courier may need to travel between a depot and a pickup point four to five times daily, each trip taking 40–60 minutes. That’s four to five hours on the road every day,” he told 36Kr.
With Rino.ai’s vehicles handling depot-to-station runs, couriers can focus on community collection and dropoff. The vehicles also operate around the clock, with remote dispatch enabling overnight deliveries. According to the company, clients have cut last-mile delivery costs by 30–50%. In one case, a courier franchisee in Wuhan reduced its per-parcel cost from RMB 0.2 (USD 0.03) to RMB 0.1 (USD 0.01) using Rino.ai’s service.
Pushing toward commercialization
“The second stage of this industry will be about rational consumption,” Zhu said. “When the fleet grows to 100,000–200,000 units, customers will care less about novelty and more about whether the product is reliable and can consistently save them money.”
He argued that reaching automotive-grade standards is key. “It’s one thing for a car to run a few times without issues. It’s another for it to run every day, 365 days a year, without incident.”
Accordingly, Rino.ai is investing its new funds in vehicle development, AI upgrades, and commercial expansion. Huang said vehicles built to automotive standards have higher asset value and better uptime and safety in real-world use.
Compared with passenger cars equipped with driver assistance systems, Level 4 autonomous logistics vehicles have simpler structures because they eliminate driver cabins and comfort features, but demand stricter safety and fault tolerance. “Our biggest challenge is still AI itself. For this industry, the key question is: how can we make the vehicle safer within controllable limits?” Huang said.
The broader sector is evolving rapidly. Assisted driving in passenger vehicles progressed from high-definition maps to transformer-based architectures using bird’s-eye view (BEV) perception, then to end-to-end models in 2024, and now in 2025, to multimodal models combining vision, language, and action (VLA) reasoning for safer, human-like decision-making.
Zhu emphasized that Rino.ai is a product company, not just a technology provider. “If a technology doesn’t improve product metrics, we won’t use it. If it does, even if it’s hard, we’ll figure it out,” he said.
Rino.ai’s upcoming vehicle is being developed as a flexible platform rather than a single model, with modular components that adapt to different use cases. “In the past, we built technology and products to fit scenarios. Now, with commercialization at scale, it’s the scenarios that are seeking out products,” Huang told 36Kr. He added that the company is also preparing vehicles with larger capacities on the new platform.
This expansion could allow Rino.ai to move beyond express parcel delivery into broader same-city freight, and eventually cross-city logistics, depending on demand.
On the sales front, Zhu said Rino.ai plans to bundle hardware with its self-driving software. “Most companies sell hardware below cost. We want to build a model where the entire lifecycle of the product generates profit.” He expects the company to reach breakeven once its fleet grows to 10,000 units.
With continued investment and targeted deployment, Rino.ai’s commercialization path is becoming clearer. As autonomous vehicles move from pilot projects to mainstream adoption, the balance between scale and cost will define the sector’s trajectory.
KrASIA Connection features translated and adapted content that was originally published by 36Kr. This article was written by Xu Caiyu for 36Kr.