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Autonomous vehicles driving into the future with LiDAR sensors

Worldwide, automation use cases are spreading rapidly across various industries and sectors, with sensor-based technologies playing a fundamental role in enhancing the reach of automation. LiDAR (Light Detection and Ranging) is one of the most promising sensor-based technologies for the applications of autonomous vehicles or self-driving cars. LiDAR technology is the building block that makes autonomous vehicles aware of their surroundings and eliminates collision risk while driving.

Definition of LiDAR technology

LiDAR is a sensing method that measures the exact distance of an object on the earth’s surface and has emerged as a popular method to calculate geospatial measurements. It uses pulsed lasers to calculate the variable distance of an object and can generate an accurate 3D map of the earth’s surface and the object under observation. It is comprised of three primary components — namely scanner, laser, and GPS receiver — and LiDAR sensors can be mounted either on helicopters (or drones) (airborne LiDAR) or moving vehicles (terrestrial LiDAR). The technology uses the time taken for the laser signal to return to the LiDAR sensor to calculate the distance of an object.

Distance of object = Speed of light x time of flight/2

Some of the popular developmental objectives of LiDAR technology include oceanography, autonomous vehicles, digital terrain models, agriculture, and archaeology.

LiDAR in autonomous vehicles

In an autonomous vehicle, the LiDAR sensor receives data from hundreds of thousands of laser pulses each second. It uses an onboard computer to analyse the ‘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 the surrounding environment, it is critical to train the onboard AI model with annotated point cloud datasets. The annotated data allows autonomous vehicles to detect, identify, and classify objects. Such image and video annotation helps an autonomous vehicle precisely detect road lanes and moving objects and analyse real-world traffic scenarios.

The usage of LiDAR technology in autonomous vehicles is no longer a matter of research. Automobile manufacturers have already started integrating LiDAR technology in advanced driver assistance systems (ADAS) to make sense of the dynamic traffic environment surrounding the vehicle. These systems make precise split-second decisions based on hundreds of careful calculations from hundreds of thousands of data points, making the journey of self-driving cars safe and secure.

Limitations of LiDAR technology

Although LiDAR technology can facilitate accurate 3D environmental mapping, its high cost hinders its advancement. And with the rapidly evolving AI models, a simple camera — significantly cheaper and smaller than LiDAR sensors — can easily accomplish the same task in autonomous vehicles. LiDAR has certain advantages over a camera, such as better distance judgement, immunity to sudden light changes, resistance to harsh weather conditions, and less susceptibility to malicious attacks. But it may fail to identify real-life driving situations and complexities of the driving environment, such as a pedestrian on the phone about to wander into traffic or a biker looking over their shoulder to join a new lane. Although, presently, camera AI applications are far from perfect, once these models become more intelligent, a combination of a simple camera and cheap radar can potentially render LiDAR technology and sensors obsolete.

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