With the acceleration of urbanization, traditional traffic signal control systems, which rely on fixed timing or single detection technologies (such as induction loops), struggle to adapt to dynamic traffic flow changes, resulting in low traffic efficiency. Breakthroughs in infrared sensor technology have provided a new solution for traffic flow monitoring at intersections. This article focuses on the technical solution design of infrared sensors in intelligent transportation, analyzing from multiple dimensions such as hardware architecture, algorithm logic, and dynamic control strategies, to reveal their core role in enhancing intersection traffic efficiency and safety.

Infrared sensors achieve non-contact detection by receiving the infrared radiation emitted by target objects (such as vehicles and pedestrians). Their core advantages lie in their all-weather operation capabilities and resistance to environmental interference. In intelligent transportation scenarios, two main types of technologies are used: generating thermal images based on the temperature differences of target objects, and analyzing the position, speed, and trajectory of vehicles through edge computing devices; infrared proximity sensors, which determine the presence of vehicles by transmitting and receiving infrared pulses and output high and low level signals to the controller to trigger signal light adjustments.

Sensor Deployment Architecture

At intersections, the deployment of infrared sensors needs to follow a specific architecture to achieve comprehensive and precise monitoring. Typically, a set of infrared sensors is installed at the entrance and exit of each lane. Taking a four-lane intersection as an example, four sensors are required at the entrance and four at the exit for each of the four lanes in one direction, totaling 16 sensors for a single direction. A total of 64 infrared sensors need to be deployed in four directions. These sensors are connected to the central data processing unit via wired or wireless methods. The sensors are arranged horizontally to ensure coverage of the entire lane width, and are generally installed at a height of 2 – 3 meters from the ground, which can avoid vehicle collisions and ensure an effective signal reception range.

Central Data Processing Unit

The central data processing unit is the core “brain” of the entire precision traffic flow monitoring solution. It is responsible for receiving data signals from various infrared sensors and conducting real-time analysis and processing. This unit is equipped with a high-performance processor capable of quickly calculating complex algorithms. For example, parallel computing technology is used to simultaneously process data from multiple sensors. Its memory capacity is at least 8GB to ensure fast storage and reading of data during processing. The data processing unit also has a data caching function. When a temporary network transmission failure occurs, it can temporarily store sensor data and transmit it after the network is restored, avoiding data loss. At the same time, it is connected to the external traffic management system through standard network interfaces, such as Ethernet interfaces, to upload the processed data in a timely manner for traffic managers to make decisions.

Solution Workflow

Infrared sensors detect vehicles by using the characteristics of infrared rays. When a vehicle enters the monitoring area of the sensor, the vehicle blocks part of the infrared rays. The transmitter inside the sensor continuously emits infrared rays, and the receiver receives the reflected infrared signals. Under normal circumstances, the intensity of the infrared rays received by the receiver remains at a stable level. When a vehicle appears, the infrared rays are blocked, and the intensity of the signal received by the receiver changes. By detecting and analyzing this change in signal intensity, the sensor can determine whether a vehicle has entered or left the monitoring area. For example, an infrared intensity change threshold is set, and when the change in the signal intensity detected by the receiver exceeds this threshold, a vehicle is determined to be present.

Once the sensor detects a vehicle, it immediately generates corresponding electrical signals. These signals contain information such as the time when the vehicle passed and the lane position. The sensor converts these electrical signals into digital signals and sends them to the central data processing unit via wired transmission methods (such as the RS – 485 bus) or wireless transmission methods (such as ZigBee wireless communication technology). In wired transmission, the RS – 485 bus has strong anti-interference capabilities and a long transmission distance, ensuring the accurate transmission of data. In wireless transmission, ZigBee technology has low power consumption and strong self – networking capabilities. Even if some sensors malfunction, the entire network can still operate normally, ensuring stable data collection and transmission.

After receiving the data transmitted by the sensors, the central data processing unit first cleans the data to remove error data caused by interference and other factors. Then, it analyzes the data using specific algorithms. For example, by calculating the time interval between two adjacent vehicle detection signals, the driving speed of the vehicle can be obtained; the traffic flow of each lane can be counted based on the number of vehicles passing through different lanes within a certain period. At the same time, machine learning algorithms are used to learn from historical data to predict the change trend of traffic flow at different time periods. For example, by analyzing the traffic flow data at the same time period of each day in the past week, the traffic flow situation at that time period of the current day can be predicted, providing a basis for the intelligent timing of traffic lights.

Technical Advantages of the Solution

Infrared sensors have extremely high detection accuracy for vehicles. Their detection accuracy can reach over 98%, and they can accurately distinguish different types of vehicles, whether they are small cars, medium-sized buses, or large trucks. This is because infrared sensors can accurately determine the size and type of vehicles based on the area and intensity change of the blocked infrared rays. Compared with traditional induction loop detection methods, infrared sensors are not affected by environmental factors such as road construction and moisture, and their detection accuracy is more stable and reliable.

Infrared sensors can monitor the dynamics of vehicles in real-time. The delay from the moment a vehicle enters the monitoring area to the moment the detection signal is transmitted to the central data processing unit is extremely short, generally within a few milliseconds. This enables traffic managers to obtain the latest traffic flow information in a timely manner and respond quickly to traffic conditions. For example, when a sudden traffic jam occurs in a certain lane, the infrared sensor can immediately detect the abnormal increase in traffic flow and quickly transmit the information to the traffic management system, so that the traffic light timing can be adjusted in a timely manner to relieve the congestion.

Infrared sensors can operate stably under various environmental conditions. Whether in the hot summer or the cold winter, their performance is not significantly affected. In 恶劣 weather conditions such as rainy and snowy days, infrared sensors can still detect vehicles normally. This is because infrared rays have a certain penetration ability and can penetrate a certain thickness of rain, fog, and snow. At the same time, the surface of the sensor is made of special materials that are waterproof, dustproof, and corrosion-resistant, enabling it to adapt to complex outdoor environments, greatly reducing maintenance costs and improving the reliability of the system.