"Deep adaptation to defined scenarios, rather than generalized application across all scenarios, is the best path for enterprise AI deployment."
On January 25, 2024, Qingzhui Logistics, together with PonyTron and Sinotrans, obtained China's first commercial operation license for autonomous-driving cross-provincial expressways (the Beijing-Tianjin-Tanggu Expressway), becoming the country's first enterprise to achieve cross-provincial commercial operation of autonomous driving on trunk-line logistics. As of September 2025, no safety accidents had occurred during its supervised autonomous-driving trunk-line commercial operation testing.
The "unreliability" of autonomous driving in passenger cars essentially stems from "insufficient generalization capability in complex scenarios": sudden situations on urban roads (such as pedestrians darting out from blind spots and detours around construction) far exceed the boundaries of model training. The core of Qingzhui Logistics' trunk-line autonomous driving lies in its "scenario-definition technology" — a dimension-reduction strike — rather than a simple reliance on fixed routes.
01 Core Contradictions in the Logistics Industry: The Coordinated Matching of People, Vehicles, and Roads
The first principle of the logistics industry is "achieving a balance of efficiency, safety, and cost in freight circulation through the coordinated matching of 'people, vehicles, and roads'." Trunk-line logistics is the "aorta" of the logistics system, yet under the traditional model the fragmentation of "people, vehicles, and roads" gives rise to three core contradictions:
1. The "People" Contradiction: Dual Pressure from Shortages and Risks
Labor crisis: Analysis of logistics operations from January to August 2024 by the China Federation of Logistics and Purchasing shows that the shortfall of trunk-line truck drivers has reached 1.2 million, with those under 35 accounting for only 25.5% of practitioners and those under 25 as low as 1.4%, while the younger generation's willingness to enter the profession continues to decline.
Safety hazards: The China Road Freight Smart Safety White Paper (2025), jointly released by G7 and PwC, points out that 85% of trunk-line accidents stem from human factors, of which fatigue driving (continuous driving over 4 hours) accounts for 62%, and aggressive driving (speeding, failing to maintain a safe distance) accounts for 77%.
2. The "Vehicle" Contradiction: Equipment Blind Spots and Control Limitations
Analysis of over 4,000 accident samples in the G7 white paper found that 35% of trunk-line accidents directly originate from vehicle blind spots, of which accidents caused by right-side blind spots account for 46% and reversing blind spots 32%. Due to body structure limitations, traditional trucks have natural constraints in the coverage of mirrors and cameras, becoming the main weak point in safe operation.
3. The "Road" Contradiction: Efficiency Loss in Cross-Provincial Dispatching
Research by the China Federation of Logistics and Purchasing in 2024 shows that the average empty-running rate of domestic trunk-line transport reaches 30%–35%, while cross-provincial dedicated lines exceed 40% due to information asymmetry and difficulty in backhaul freight matching. Taking the Beijing-Tianjin-Tanggu line as an example, traditional manual dispatching requires 2–3 hours to match cargo, and it is difficult to adjust routes in response to sudden road conditions, leading to large fluctuations in timeliness.
02 Qingzhui AI Autonomous Driving: Defining Technology by Scenario, Crushing Experience with Data
Qingzhui Logistics' solution does not pursue the technical showmanship of "full driverless," but instead customizes an L4-level autonomous driving system targeting the above contradictions, with core functions focused on two dimensions: safety and efficiency:
1. Resolving Human Risk: Ultra-Long-Range Perception and Blind-Spot Coverage
Fatigue-driving replacement: Equipped with Pony.ai's third-generation autonomous driving system, a forward perception module composed of one ultra-long-range camera, one long-range LiDAR, and one long-range millimeter-wave radar can detect obstacles 1,000 meters away. At a highway speed of 90 km/h, it can identify a disabled vehicle ahead and plan an avoidance route 30 seconds in advance, completely eliminating the reaction-delay risk caused by fatigue driving.
Blind-spot visualization technology: A 360° panoramic perception module containing 2 LiDARs, 3 blind-spot-complementing LiDARs, and multiple cameras achieves blind-spot-free coverage within 200 meters of the vehicle body. In particular, a dual-monitoring algorithm is designed for right-side and reversing blind spots, with a blind-spot accident warning accuracy of 99.8%.
2. Improving Cross-Provincial Efficiency: Intelligent Routing and Capacity Coordination
Cross-provincial expressway operation network: In 2024, it was approved for a 100-kilometer test route on the Beijing-Tianjin section of the Beijing-Tianjin-Tanggu Expressway, achieving China's first cross-provincial commercial operation of autonomous-driving heavy trucks. The system dynamically optimizes routes based on real-time traffic data, shortening one-way transport timeliness by 15% compared with manual driving.
Logistics network coordination: Connected to Sinotrans' national logistics network, AI algorithms match round-trip cargo, reducing the empty-running rate by 28% compared with the industry average during the testing period.
As a typical case of AI deployment in trunk-line logistics, Qingzhui Logistics' core practical logic is "deep adaptation within defined scenarios": through fixed routes, standardized cargo, and progressive validation, it has achieved the transformation of autonomous driving technology from "lab testing" to "commercial operation."
1. Dynamic Route Planning to Reduce Detour Costs
For "fixed routes," AI optimizes paths by combining real-time data: Qingzhui Logistics' AI integrates real-time traffic flow and construction data on the Beijing-Tianjin-Tanggu Expressway, dynamically adjusting driving lanes and speeds, improving passage efficiency by 20% and shortening single-trip transport time by 1.5 hours;
2. Vehicle-Road Collaboration to Transmit Information and Eliminate Passage Blind Spots
For "lagging roadside information," Qingzhui Logistics, together with Tianjin Port, advances vehicle-road collaboration: deploying roadside cameras and UWB positioning base stations on the Beijing-Tianjin-Tanggu Expressway to relay construction and accident information in real time and push warnings to autonomous vehicles 3 kilometers in advance; at the Tianjin Port zone, "centimeter-level positioning" is achieved, enabling seamless switching of vehicles between "highway" and "port" modes, reducing blind-spot positioning error to within 1 meter and improving in-port passage efficiency by 25%.
In terms of commercialization progress, Qingzhui Logistics advances deployment through three progressively validated stages
Amid the wave of intelligent transformation in the logistics industry, autonomous driving technology is moving from the laboratory into a critical stage of commercial operation. As a smart logistics platform jointly built by Sinotrans and Pony.ai, Qingzhui Logistics has constructed a commercialization model for L4-level autonomous-driving heavy trucks in the trunk-line logistics domain through a progressive path of "technology iteration — scenario validation — value transformation."
03 Lessons: Defining Technology by Scenario, Crushing Experience with Data
The essence of logistics AI is not to replace people, vehicles, and roads, but to bridge the collaborative gaps among the three through data, making the value of every link quantifiable and implementable. The core competitiveness is "data collaboration" rather than single-point technology: the value of logistics AI does not depend on model parameters, but on the ability to integrate "people (behavior data), vehicles (status data), and roads (environment data)"; technological breakthroughs in a single link are difficult to implement. Deep adaptation to defined scenarios rather than generalized application across all scenarios: successful cases all focus on niche scenarios to avoid the "maladaptation" of general-purpose AI — Qingzhui locks onto "Beijing-Tianjin-Tanggu Expressway + steel transportation," using scenario certainty to reduce technical difficulty.
The lesson from Qingzhui Logistics is this: true industrial AI is not a parameter competition in the laboratory, but a triple crush of "scenario — technology — operation."
