Across the globe, automation use cases are rapidly spreading across different industries and sectors, and sensor-based technologies play a fundamental role in expanding the scope of automation. LiDAR (Light Detection and Ranging) is one of the most promising sensor-based technologies for self-driving cars or self-driving car applications, making self-driving cars aware of their surroundings and eliminating the risk of collisions while driving. becomes a component.
What is LiDAR technology?
LiDAR, a sensing method that measures the precise distance of objects on the Earth’s surface, is emerging as a popular method for computing geospatial measurements. Pulsed lasers can be used to calculate variable distances to objects and generate accurate 3D maps of the Earth’s surface and observed objects. He consists of three main components: scanner, laser and GPS receiver. LiDAR sensors can be mounted either on helicopters or drones (airborne LiDAR) or on moving vehicles (ground LiDAR). The technology uses the time it takes for a laser signal to return to his LiDAR sensor to calculate the distance of an object.
distance to object = speed of light x time of flight/2
Common development goals for LiDAR technology include oceanography, self-driving vehicles, digital terrain models, agriculture, and archaeology.
LiDAR for self-driving cars
In self-driving cars, LiDAR sensors receive data from hundreds of thousands of laser pulses per second. An on-board computer is used to analyze a ‘point cloud’ of laser reflection points and animate a 3D representation of the surrounding environment. To ensure that LiDAR can create an accurate 3D representation of the surrounding environment, it is important to train the onboard AI model with an annotated point cloud dataset.of annotated data Enable self-driving cars to detect, identify and classify objects.like that Image and video annotation It helps autonomous vehicles accurately detect lanes and moving objects and analyze real-world traffic scenarios.
The use of LiDAR technology in self-driving cars is no longer a matter of research. Automakers have already started integrating LiDAR technology into advanced driver assistance systems (ADAS) to understand the dynamic traffic environment surrounding their vehicles.
Based on hundreds of thousands of careful calculations from hundreds of thousands of data points, these systems make split-second, accurate decisions to ensure that self-driving cars travel safely and reliably.
Limitations of LiDAR technology
LiDAR technology facilitates accurate 3D environment mapping, but its high cost hinders progress. With rapidly evolving AI models, simple cameras (significantly cheaper and smaller than LiDAR sensors) can easily perform the same tasks in self-driving cars.
LiDAR has certain advantages over cameras, such as better distance judgment, resistance to sudden light changes, resistance to harsh weather conditions, and resistance to malicious attacks. However, it may not be able to discern the complexity of real driving situations and driving environments. For example, a pedestrian on a phone trying to stray into a lane, or a cyclist trying to enter a new lane over his shoulder.
Today, camera AI applications are far from perfect, but as these models become more intelligent, combining simple cameras with cheap radar could make LiDAR technology and sensors obsolete.
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