In Changsha, China — a vibrant city with approximately 3.52 million registered motor vehicles, continuously facing traffic congestion pressures — the Jinxing Avenue-Yuelu Avenue intersection was once a notorious local traffic bottleneck. As a critical hub connecting the Hexi district to the main urban area, it suffered from frequent congestion due to lane configuration, mixed traffic flow, and other issues.

Data from 2022 indicated that during peak hours at this intersection, the average delay per vehicle reached 3.8 minutes, with severe daily congestion lasting over 8 hours, and the accident rate was higher than surrounding areas. Local residents often reported delays of up to twenty minutes during peak periods, as signal timing struggled to adapt to real-time traffic flow, leading to chaotic vehicle weaving and poor travel experience.

Pain Point Analysis – Four Major Management Challenges of Traditional Intersections
Before the upgrade, this intersection epitomized the typical dilemmas of Changsha's traditional traffic management. The core problems can be summarized into four key areas:

"Blind" Management: Changsha's frequent fog, rain, and high temperatures caused the accuracy of the original surveillance systems to plummet at night and in harsh weather, failing to provide reliable data for signal control.
 

"Inaccurate" Optimization: Traditional signal timing relied on historical experience rather than real-time data, severely misaligned with dynamic traffic demand. Although Changsha optimized 190 intersections and sections and implemented green-wave coordination on 36 main roads, intersections lacking holographic perception remained bottlenecks.
 

"Delayed" Response: The time lag from occurrence to detection for incidents like traffic accidents or illegal parking was too long. For instance, traffic chaos caused by illegal parking on Xiangpu Road required patrols or complaints for intervention, posing a high risk of secondary accidents.
 

"Costly" Maintenance: Maintaining traditional inductive loops required repeated road cuts, incurring high costs and causing public nuisance—issues the "Municipal Road Quality Improvement" initiative aimed to avoid.
 

Multi-Dimensional Comparison of Market Solutions – Why Radar-Video Fusion is the Optimal Choice?

The Breakthrough Solution – Radar as the Foundation, Fused with Vision, Granting Intersections an "All-Seeing Eye"

Nanoradar's solution aims to build an "unstoppable perception brain" for Changsha's intersections, resonating with the city's ongoing smart traffic initiatives like "Intelligent Connected Signal Control Intersections" and "Robotic Tidal Flow Lanes."

Radar as the "Bones" – Constructing a Precise and Reliable Spatial Framework

All-Weather Reliability: Effectively handles Changsha's rain, fog, and haze, providing stable, centimeter-accurate target position, speed, and trajectory data 24/7, compensating for visual limitations.

All-Target Perception: Unaffected by lighting, accurately detects motor vehicles, non-motorized vehicles, and pedestrians, providing a data foundation for optimizing "pedestrian-vehicle separation."

Vision as the "Skin" – Providing Clear, Traceable Identity Features

High-definition cameras capture rich characteristic information such as license plates, vehicle models, and color for targets locked by radar, completing the loop for enforcement and non-contact evidence collection.

Edge Fusion as the "Brain" – Enabling Intelligent Localized Decision-Making

Built-in high-performance edge computing units perform real-time spatiotemporal synchronization, data fusion, and incident judgment of radar and video data locally, granting intersections local intelligence. This significantly reduces cloud burden, boosts response speed to milliseconds, and directly enables real-time signal optimization.

Smart Traffic Radar-Video System: A Three-Step Practical Guide from Agile Deployment to Presentation
(I) Agile Deployment – Intersection Setup in 2 Hours with Zero Service Disruption

Equipment Layout: One millimeter-wave radar + one high-definition camera installed on each of the four standard signal poles at the intersection corners, leveraging the radar's 90° horizontal field of view to cover approach lanes, exit lanes, and the central conflict zone.

Key Installation Parameters:

Mounting Height: 5-7 meters (perfectly aligns with Changsha's existing signal pole heights).

Tilt Angle: Recommend a downward tilt of 0°-9° to optimize the detection zone.

Deployment Efficiency: Full equipment installation and commissioning for a single intersection can be completed within 2 hours, minimizing traffic impact and avoiding congestion exacerbation.

(II) System Architecture: A Layered Structure Compatible with Existing Systems and Scalable

Front-End Perception Layer: Integrates 80GHz high-performance millimeter-wave radar, high-definition cameras, and edge computing units for data collection and local fusion.

Network Transmission Layer: Processed structured data is transmitted via fiber optic or 5G/4G networks with low latency and encryption.

Platform Application Layer:

Signal Control Optimization: Data directly interfaces with Changsha's existing traffic signal controllers, supporting dynamic timing and green-wave coordination.

Cloud Control Platform: Enables area-wide situational display, historical data analysis, device operation and maintenance, and report generation, assisting Changsha traffic police in "visualized command."

Strategic Scalability: Seamlessly integrates V2X communication modules, supporting Changsha's future vehicle-infrastructure cooperation and autonomous driving initiatives.

(III) System Presentation – Real-Time Fusion of All-Element Data and Historical Playback

When a target vehicle enters the detection zone, video surveillance and radar sensor data are superimposed in real-time. Virtual loop triggers initiate capture, and the system automatically records the vehicle's passage time, lane number, license plate information, vehicle color, speed, and generates structured data stored in the database. The storage system supports time-indexed queries, allowing users to retrieve historical data records using time parameters.

Customer Benefits – Multi-Dimensional Transformation from a "Cost Center" to a "Value Hub"
Operational for over 16 months, this project has delivered significant results across management, economic, and social dimensions, successfully transforming a congested intersection into a smart hub.

(I) Management Value: Data-Driven, Efficiency Multiplied

Traffic Efficiency Soars: Dynamic signal timing reduced average morning peak delay by 34% (from 3.8 to 2.5 minutes per vehicle). Daily congestion duration dropped by 55% (from 10 to 4.5 hours).

Fully-Visualized Command: Traffic police gain real-time holographic intersection awareness via the platform, achieving "zero paralysis" in bad weather. Related citizen complaints decreased by 90%.

Accurate and Efficient Enforcement: Automated capture of violations generates over 11,000 valid evidence entries annually, boosting enforcement efficiency tenfold, with zero administrative reconsideration cases.

(II) Economic Value: Cost Reduction, Revenue Generation, Significant Benefits

Drastically Reduced O&M Costs: Devices support remote operation and boast high reliability. Annual operation and maintenance costs decreased by 86% compared to traditional models.

Regional Economic Vitality Boosted: Smoother traffic increased weekend foot traffic to nearby malls by 25% and improved logistics delivery efficiency by approximately 20%, injecting vitality into the regional economy.

(III) Social Value: From "Congestion Pain Point" to "Livability Highlight"

Enhanced Traffic Safety: Millisecond-level incident response reduced intersection accident rates by 45%. Public sentiment shifted from "relying on luck" to "having confidence."

Building a Foundation for Future Intelligence: The system's continuous, high-precision trajectory data provides core data support for Changsha's "Urban Traffic Brain" and autonomous driving development, holding profound strategic value.

 Millimeter-Wave Radar Technical Parameters