“Multi-Source Perception, Three-Dimensional Monitoring and Coordinated Emergency Response”: Genvict Proposes a New Paradigm for Expressway Safety Governance

Guided by strategic policies such as the Outline for Building a Strong Transportation Nation and the 14th Five-Year Plan for Digital Transportation Development, China’s expressway sector has entered a phase of deeper digital transformation and intelligent upgrading. However, operational road safety remains the central issue and continues to pose multiple technical and management challenges.

Focusing on the status and key bottlenecks of critical technologies for expressway monitoring, early warning, and response, the research team at Shenzhen Genvict Technologies Co., Ltd. systematically proposes an integrated technology and equipment system built on “multi-source perception, three-dimensional monitoring, and coordinated emergency response.” It provides a structured technical solution for high-quality smart expressway development.

1. Three Bottlenecks: Practical Challenges Constraining Upgrades to Safety Governance

As expressway operational safety management undergoes digital upgrading, it faces the following key challenges:

Bottleneck 1: Difficulty integrating multi-source heterogeneous data. Cameras, millimeter-wave radar, LiDAR, ETC gantry antennas, and other sensing devices use different data formats and generate highly redundant information, making it difficult to produce a unified, high-quality perception data stream.

Bottleneck 2: Limited generalization of intelligent algorithms. Under extreme conditions such as rain, fog, nighttime, intense backlighting and localized dense fog, the detection accuracy and recall of existing deep learning models decline significantly. Identifying anomalous events in long-tail scenarios remains a technical challenge.

Bottleneck 3: Inefficient cross-agency coordination. The three parties responsible for expressway operations (traffic police, road administration authorities, and operators) do not yet have a well-developed information-sharing mechanism, resulting in relatively low cross-agency response efficiency. A model centered on post-event response cannot meet the expressway safety management requirement for “rapid detection and rapid response.”

2. Advances in Key Technologies: A Multi-Dimensional Review of Technology Pathways

The following is a systematic review of current mainstream technologies across the complete monitoring, early warning, and response chain.

For traffic-flow monitoring and early warning, inductive-loop detectors offer high accuracy but involve high installation and maintenance costs; video monitoring provides rich information but is constrained in adverse weather; radar is robust against interference but has limited ability to detect small vehicles; and ETC data is reliable but depends on gantry coverage.

At the data analytics layer, deep learning has surpassed traditional statistical methods to become the mainstream approach, but it requires large volumes of labeled data and entails high computational costs. Different models also have distinct strengths: convolutional neural networks (CNNs) are better suited to traffic-flow data with clear spatial structures, while recurrent neural networks (RNNs) excel at capturing dynamic changes over time.

For subgrade hazard monitoring and early warning, fiber-optic sensing, satellite remote sensing and elastic-wave monitoring each offer distinct advantages. As a non-invasive method, elastic-wave monitoring combines AI-based deep learning algorithms with feature extraction and pattern recognition of wavefield signals, enabling early warning of hidden risks such as voids and cracks beneath the subgrades of in-service expressways. It therefore offers considerable application potential.

For event detection, mainstream AI algorithms such as Faster R-CNN, the YOLO family, and RT-DETR each have suitable applications in expressway scenarios. Multimodal fusion using “radar + video + ETC” provides complementary strengths for all-weather monitoring, while supplementary methods such as infrared detection and ETC-assisted detection are suited to specific scenarios.

For emergency response, the Traffic Guardian system uses existing Dedicated Short-Range Communication (DSRC) links within the Electronic Toll Collection (ETC) system to broadcast warnings rapidly. Variable message signs and mobile apps form a multilayer information dissemination system, although further improvements are required in unified information standards, cross-platform synchronization mechanisms, and communications reliability under extreme conditions.

3. Core Solution: Building the “Multi-Source Perception, Three-Dimensional Monitoring and Coordinated Emergency Response” System

Based on existing technologies and practical pain points, the research proposes a technology and equipment framework spanning all scenarios, all elements and the full lifecycle.

Architecture of the “Multi-Source Perception, Three-Dimensional Monitoring and Coordinated Emergency Response” Technology and Equipment System

Perception Layer - Integrating Multi-Source Heterogeneous Data to Establish a Highly Robust Perception Foundation

The perception layer integrates multi-source heterogeneous sensing data from cameras, millimeter-wave radar, LiDAR, ETC radio frequency systems and elastic-wave detectors (geophones). Built on a domain-specific multimodal foundation model for roadway scenarios and a fusion architecture spanning vision, point-cloud and time-series data, the layer establishes a three-tier “cloud-edge-device” collaborative inference framework. The cloud-based foundation model handles in-depth understanding of complex scenarios and continuous model iteration and optimization, while lightweight models deployed at the edge and device levels deliver millisecond-level real-time inference. This architecture significantly strengthens perception robustness in extreme environments including rain, fog, nighttime, intense backlighting and localized dense fog, improving the accuracy and recall of traffic-event and subgrade-hazard recognition. Combined with low-power integrated hardware technology, it enables a portfolio of intelligent perception terminals that can be flexibly deployed on unmanned aerial vehicles (UAVs), roadside systems and intelligent connected vehicles.

Monitoring Layer - Integrating Low-Altitude, Roadside and In-Vehicle Monitoring for Network-Wide, Around-the-Clock Coverage

The monitoring layer establishes a three-dimensional monitoring network covering all scenarios (including curves, localized dense-fog zones and nighttime conditions), all elements (pavement, subgrade, vehicles and the environment), and the full lifecycle (routine inspection, anomaly warning and emergency response):

• Low-altitude dimension: UAVs equipped with high-precision optical and multispectral sensors perform centimeter-level inspections of subgrade defects and rapidly deploy to traffic incidents for close-range identification, creating dynamic air-ground complementarity with ground systems;

• Roadside dimension: high-risk road sections are equipped with “radar-RF-video integrated” edge perception units (fusing millimeter-wave radar, ETC and video), together with elastic-wave detectors. AI algorithms precisely analyze hidden risks beneath the subgrade, including water seepage and void development, and issue tiered warnings;

• In-vehicle dimension: intelligent upgrades to MOT-standard vehicle terminal cameras and in-vehicle terminals create a probe-vehicle perception network. It detects pavement and subgrade defects and anomalous traffic events in real time, providing mobile supplementary perception nodes.

Emergency Response Layer - Combining Institutional Coordination with Intelligent Equipment Clusters to Transform the Emergency Management Model

At the emergency response layer, the proposed system addresses both organizational structures and equipment:

Organizational dimension: strengthen the institutionalized three-party coordination mechanism among traffic police, road administration authorities and operators. A unified cloud command platform integrates their data resources and response authorities, enabling intelligent incident assignment, cross-agency work-order routing and closed-loop management of the response process. This provides the organizational foundation for efficient emergency response.

Equipment dimension: establish an embodied-intelligence equipment cluster for emergency response based on “heterogeneous multi-machine collaboration.” In low-altitude airspace, emergency-supply transport UAVs, personnel-transport UAVs and emergency-command UAVs form a three-dimensional aerial operations unit. On the ground, unmanned vehicles for emergency-supply transport, unmanned vehicles for personnel transport and emergency-command robots create a flexible support network. Each type of equipment incorporates an edge-intelligence module and can perceive its environment, make autonomous decisions and collaborate as a group. Through real-time interaction with the command platform over 5G-A/6G networks, the cluster supports an integrated air-ground workflow in which command UAVs coordinate from the air, transport equipment delivers supplies precisely and transfer equipment conducts efficient rescue operations. This system shifts expressway emergency management from a human-led model to collaborative clusters of intelligent agents.

4. Outlook

As the “AI + Transportation” initiative advances, the system will evolve more rapidly from “passive perception” to “proactive early warning” and ultimately “adaptive response.” On one front, domain-specific multimodal foundation models and embodied intelligence technologies will move perception terminals from passive data collection to active cognition, enabling early simulation of subgrade hazards and timely prediction of traffic events. On another, the convergence of 5G-A/6G, digital twins, and blockchain will establish a secure and trusted foundation for collaborative governance with virtual-physical mapping. This will support deeper integration between the three-dimensional monitoring network and multi-party coordinated operations, advancing expressway operational safety governance from individual intelligence to collective intelligence and from human-driven processes to human-machine symbiosis.

Evolution of Expressway Operational Safety Management