Globally, automation use cases are spreading rapidly across industries and sectors, with sensor-based technologies playing a fundamental role in increasing the scope of automation. LiDAR (Light Detection and Ranging) is one of the most promising sensor-based technologies that can be used in autonomous or self-driving cars, becoming a cornerstone for autonomous vehicles to understand their surroundings while driving and eliminate the risk of collisions.
What is lidar technology?
LiDAR is a sensing method that measures the precise distance of objects from the Earth’s surface and has become a popular method for computing geospatial measurements. It uses a pulsed laser to calculate variable distances to objects and can generate precise 3D maps of the Earth’s surface and objects being observed. It consists of three main components: scanner, laser and GPS receiver. Lidar sensors can be mounted on helicopters or drones (airborne lidar) or on mobile vehicles (ground lidar). The technology uses the time it takes for the laser signal to return to the lidar sensor to calculate the distance to an object.
Object distance = speed of light x flight time / 2
Some of the popular development targets for lidar technology include oceanography, self-driving cars, digital terrain modeling, agriculture, and archaeology.
Lidar in self-driving cars
In self-driving cars, lidar sensors receive data from hundreds of thousands of laser pulses per second. It uses an onboard computer to analyze a “point cloud” of laser reflection points to animate a 3D representation of the surrounding environment. To ensure that LiDAR can create an accurate 3D representation of its surroundings, it is critical to train an airborne AI model using an annotated point cloud dataset.This Annotation data Allows self-driving cars to detect, recognize and classify objects.Such Image and Video Annotation Help autonomous vehicles accurately detect road lanes and moving objects and analyze real-world traffic scenarios.
Using lidar technology in self-driving cars is no longer a research problem. Automakers have begun to integrate LiDAR technology into advanced driver assistance systems (ADAS) to understand the dynamic traffic environment around the vehicle.
These systems make precise moment-to-moment decisions based on hundreds of careful calculations from hundreds of thousands of data points, making autonomous vehicle journeys safe and secure.
Limitations of LiDAR Technology
Although LiDAR technology can facilitate accurate 3D environment mapping, its high cost hinders its progress. With the rapid development of AI models, a simple camera—much cheaper and smaller than a lidar sensor—could easily accomplish the same task in a self-driving car.
Compared to cameras, lidars offer certain advantages, such as better distance judgment, immunity to sudden light changes, resistance to severe weather conditions, and lower susceptibility to malicious attacks. But it may fail to recognize real-life driving situations and the complexities of the driving environment, such as a pedestrian on a phone about to break into traffic, or a cyclist looking back to join a new lane.
While current camera AI applications are far from perfect, once these models become smarter, the combination of simple cameras and cheap radar could make LiDAR technology and sensors obsolete.
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