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In recent years, the continuous increase in private car ownership has made road traffic safety an unavoidable public issue—globally, approximately 1.24 million people die in traffic accidents each year, and 20 to 50 million are injured or disabled, with collisions being the most prevalent form of traffic accident. Besides regulating driving behavior and strengthening law enforcement, improving vehicle safety performance, especially active safety technologies, has become one of the core directions for reducing accident risks.
In fact, the concept of “active safety” is not limited to the automotive field; it is also reflected in industrial equipment. For example, safety performance is a core indicator for large equipment such as cranes. For instance, the FEM standard single-girder gantry crane adopts the European FEM design standard, resulting in superior performance in terms of structural stability and operational precision. This reduces the risk of collisions and overturning during operation from the design stage, which is consistent with the logic of active collision avoidance for vehicles—using technical standards to improve the safety tolerance of the equipment itself.
Vehicle Safety Performance: The Evolution from Passive Buffering to Active Prevention
Traditional vehicle safety primarily relies on passive protection, with features like seatbelts, airbags, and crash beams essentially buffering damage after a collision. Today’s active safety features, however, prevent accidents before they happen through intelligent technologies – such as intelligent collision avoidance systems. These systems monitor road conditions in real time, predict risks, and proactively warn or intervene before danger occurs, reducing the probability of collisions at their source.
A complete intelligent collision avoidance system typically consists of an information acquisition module, a data processing module, and an execution module. “Information acquisition” is the core component, and various collision avoidance technologies act as the sensory nerves of this process.
Mainstream Vehicle Collision Avoidance Technologies: Principles and Scenarios for Adaptation
Currently, vehicle collision avoidance technologies are mainly based on different sensing principles, each with its own applicable scenarios and limitations:
1. Ultrasonic Collision Avoidance: A Low-Cost Solution for Close-Range Scenarios
Ultrasonic waves are mechanical waves with frequencies higher than 20kHz. Their collision avoidance principle uses a pulse-echo method of “emission-reflection-reception” for distance measurement: the sensor emits an ultrasonic signal and starts timing; it stops timing when the echo from an obstacle is received. The distance is calculated by combining this with the speed of sound.
It can adapt to complex environments with low brightness and dust, has a fast detection speed, simple structure, and low cost, and is insensitive to color and illuminance. Therefore, it is often used in reversing collision avoidance systems. However, multiple sensors are prone to mutual interference, the detection distance is only in the tens of meters range, there are blind spots at close range, and it can only detect single distance information.
2. Infrared Collision Avoidance: A High-Precision Choice for Long-Range Scenarios
Infrared collision avoidance is based on the principle of triangulation: an infrared emitting device (such as an infrared LED) emits a beam of light, which is reflected by the obstacle and received by a CCD detector. The distance is calculated by the beam angle and offset. Compared to ultrasound, its sensors do not have mutual interference issues, resulting in higher measurement accuracy, faster ranging speed, and longer coverage. However, direct sunlight significantly affects its measurement accuracy, and reliability decreases in bright sunlight.
3. Laser Collision Avoidance: A Stable and Efficient Technical Solution
Laser collision avoidance uses pulse ranging, similar in principle to ultrasound—after emitting a laser pulse, the distance is calculated by determining the time difference between the emitted and received echoes.
Laser has good linearity and high energy density, resulting in high ranging accuracy, fast speed, and stable operation, making it a popular choice for many high-end vehicles. However, it is greatly affected by severe weather conditions such as heavy rain and dense fog, and the visibility of lasers limits its application scenarios on roads. Furthermore, the emitted energy must be controlled within the range tolerable for the human eye.
4. Millimeter-Wave Collision Avoidance: A Versatile Technology for Complex Environments
Millimeter waves are electromagnetic waves with wavelengths of 1-10mm, operating in the 30-300GHz frequency band. They employ Frequency Modulated Continuous Wave (FMCW) detection: a continuously modulated signal is emitted, and the echo and transmitted signal are mixed to simultaneously acquire the target’s distance and speed.
This is currently one of the most adaptable technologies: small size, high spatial resolution, large ranging range, and the ability to penetrate fog, smoke, and other adverse weather conditions, making it an ideal choice for high-speed driving scenarios. However, it is prone to electromagnetic interference with other devices, and the system cost is relatively high.
5. Machine Vision Collision Avoidance: A Multi-Dimensional Perception Solution
Machine vision simulates human eye function through cameras: after acquiring road condition images, target detection (e.g., feature extraction, model-based methods) and distance calculation (e.g., single-frame static image ranging) are performed to obtain information such as obstacle shape and distance, and then the system makes a collision avoidance decision.
It is small in size, low in power consumption, and has a wide field of view, enabling it to acquire massive amounts of environmental information; however, it has high requirements for hardware and software, requires a large amount of computation, has poor real-time performance, and its performance degrades significantly in environments such as rain, fog, and strong light.
Similar “perception + decision-making” safety logic is also used in small industrial equipment: for example, a 10 ton gantry crane is equipped with travel limiters and collision warning devices. Sensors monitor the operating trajectory in real time, and automatically decelerate or stop when approaching an obstacle, improving work efficiency and avoiding collision damage between equipment.
Collision Avoidance Model: Quantification and Execution of Safe Distance
With the perception of sensor technology, it is necessary to convert information into action commands through collision avoidance models—the core of which is to establish a safe distance threshold to ensure that vehicles and obstacles are always within a safe range.
- Longitudinal Collision Avoidance Model: For rear-end collisions, it analyzes the vehicle braking process (including driver reaction time, brake response time, etc.) and calculates the critical safe following distance. When the detected actual distance is less than the threshold, the system triggers an audible and visual alarm or even automatic braking.
- Lateral Collision Avoidance Model: For lane changing and lane departure scenarios, this model combines highway technical standards and vehicle speed (the higher the speed, the greater the required lateral distance) to determine the safe lateral distance and provide timely warnings of vehicle deviation risks.
Technological Trends: Safety Upgrades Through Multi-Sensor Fusion
The limitations of single sensors have made multi-sensor information fusion an industry trend. For example, the combination of millimeter-wave radar and machine vision leverages the precise ranging advantages of millimeter waves while acquiring rich environmental information through machine vision. Furthermore, the integration of machine vision with technologies such as GPS and GIS expands the possibilities for collision avoidance through “vehicle-to-infrastructure” collaboration.
From passive buffering to active prevention, the evolution of vehicle collision avoidance technology essentially uses the perception and decision-making capabilities of technology to build an invisible safety barrier for travel—and with the deepening of multi-sensor fusion and artificial intelligence technologies, this barrier will only become more robust.