LIDAR Sensor in Autonomous Vehicles: Why it is Important for Self-Driving Cars?

August 6, 2020 4 min read By Cogito Tech. 1160 views

Sensor-based technologies are playing a key role in making Artificial Intelligence (AI) possible in various fields. LiDAR is one of the most promising sensor-based technology, used in autonomous vehicles or self-driving cars. It was essential for such autonomous machines to get aware of their surroundings and drive properly without any collision risks.

Autonomous vehicles already use various sensors and LiDAR is one of them that helps them to detect the objects in-depth. So, right here we will discuss LiDAR technology, how it works, and why it is important for autonomous vehicles or self-driving cars.

What is LiDAR Technology?

LiDAR stands for Light Detection and Ranging. It is a kind of a Remote Sensing technology that uses light in the form of a pulsed laser to measure ranges (variable distances) to the Earth. These light pulses—combined with other data recorded by the airborne system — generate precise, three-dimensional (3D) information about the shape of the Earth, its surface characteristics, and various objects visible there.

How Does LIDAR Work in Cars?

When observed from distance, LIDAR functions very similarly to sonar systems that emit sound waves which travel outward in all directions until they make contact with an object, resulting in a resonating sound wave that is redirected back to the source. The distance of that object is then calculated based on the time it took for the echo to return, in relation to the known speed of sound.

LiDAR systems also operate under this same principle with reference to the speed of light – more than 1,000,000 times faster than the speed of sound. Instead of producing sound waves, they hence transmit and receive data from hundreds of thousands of laser pulses every second. An onboard computer records each laser’s reflection point, converting this rapidly updating “point cloud” into an animated 3D representation of its surroundings.

Also Read: How to Improve Computer Vision in AI Drones Using Image Annotation Services

There are three main components of a LiDAR instrument — the scanner, laser, and GPS receiver. While other elements that play a vital role in the data collection and analysis are the photodetector and optics. Nowadays, most governments and private organizations use helicopters, autonomous flying, and airplanes for acquiring LiDAR data.

Use of LIDAR in Autonomous Vehicles

In the automotive industry, radar has long been utilized to automatically control speed, braking, and safety systems in response to sudden changes in traffic conditions. Nowadays, auto manufacturers have started to integrate LIDAR into Advanced Driver Assistance Systems (ADAS) to visualize the ever-changing environments their vehicles are immersed in.

The bunch of useful datasets from automotive platform incorporation can allow ADAS systems to make hundreds of carefully calculated driving decisions each minute precisely. We are accepting this technology as a key component in developing the new driver assistance features that can guide in delivering self-driving cars with full autonomous features with a safe and secure journey.

How LiDAR is Making Self-Driving Cars Safer?

As we know, LiDAR is a detection system like radar that uses light waves instead of radio waves to detect objects, characterize their shape, and calculate their distance. Lidar goes even further: it detects the movement and velocity of distant objects, as well as the vehicles, their own motion relative to the ground, and various other objects around it.

Hence, LiDAR-based 3D sense is an extremely indispensable technology for enabling the evolution from driver assistance to fully autonomous vehicles. LiDAR helps to gather critical data about the environment’s surrounding that ADAS requires offering reliable safety.

how lidar is making self driving cars safer

As the functioning of vehicles are becoming more autonomous and taking over the key additional driving functions, ADAS will become increasingly dependent upon LiDAR to enhance perception capabilities in all types of operating conditions.

Why LiDAR is Important for Autonomous Vehicle?

Without a precise and fast object detection system, working of an autonomous vehicle is not possible. LiDAR is making this possible with a continuously rotating LiDAR system that sends thousands of laser-pulses every second. These pulses collide with the surrounding objects and reflect.

Also Read: Why Self-driving Cars Taking Too Much Time: Challenges of Autonomous Vehicles

Further, these light reflections are then used to create a 3D point cloud. An onboard computer records each laser’s reflection point and translates this rapidly updating point cloud into an animated 3D representation created through 3D point cloud annotation to make such objects recognizable to autonomous cars through LiDAR sensors.

3D Point Cloud Labeling Service for LIDAR Annotation

To make the LiDAR sensors detect or recognize the objects, it is important to train the AI model with a huge amount of annotated images generated through the LiDAR sensors. LIDAR point cloud segmentation is the most precise technique used to classify the objects having the additional attribute that a perception model can detect for learning. point cloud to detect objects with 3d boxes
The data annotation for LiDAR helps to detect the road lane and track the object with a multi-frame, helping the self-driving car to detect the lane more precisely and understand the real scenarios around it. The best part with LiDAR is cloud annotation, an object up to 1 cm can be annotated with 3D boxes labeling the objects at every single point.

Also Read: Top Four Myths About Outsource Data Annotation Services

Cogito is one of the leading data annotation companies providing image annotation services to AI companies looking for the right training data sets for their machine learning models. Cogito works with the annotation team having an enriching experience of working with point cloud data, 3D Object tracking with 2D mapping, semantic segmentation of point cloud data with applications in intelligent vehicles, and autonomous terrain mapping and navigation.

If you wish to learn more about Cogito’s data annotation services,
please contact our expert.