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 and became essential for such autonomous machines to get aware of its surroundings and drive properly without any collision risks.

Autonomous vehicles already use various sensors and LiDAR is one of them that helps 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 is a kind of remote sensing technology using the 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, it’s 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 that travel outwards in all directions until making 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.

Actually, LiDAR systems operate under this same principle, and to do that the speed of light – more than 1,000,000 times faster than the speed of sound. Instead of producing sound waves, they 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 of the government 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) in order 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 journey.

How LiDAR is Making Self-Driving Cars Safer?

As we know LiDAR is a detection system similar to 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, own motion relative to the ground, and various other objects around it.

Hence, LiDAR-based 3D sense is a very 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.

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, 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 back.

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 LiDARs sensors detect or recognize the objects, it is important to train the AI model with a huge amount of annotated images generated through the LiDARs sensor. 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.

The data annotation for LiDARs helps to detect the road lane and tracking the object with a multi-frame helping the self-driving car detect the lane more precisely and understand the real scenarios around. And the best part is with LiDARs cloud annotation, the 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 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.

As per the international organization reports, globally more than fifty percent population is living in urban areas, and by 2050, more than two-third population will live in cities providing big investment opportunities of tech development companies.

As per the International Data Corporation report, by 2021, the spending on smart cities likely to reach more than $130 billion to make the urban areas and cities more livable with advance infrastructural facilities and society management system.

AI in smart cities is going to play big role in making urbanization smarter aim to be sustainable growth making the cities equipped with advance features to live, walk, shop and enjoy a safe and more convenient life in such environment.

Actually, while developing cities to make smart, several challenges like administration, sanitation, traffic congestion, security surveillance, parking management and many more that AI can help to provide a sustainable solution to habitants.

APPLICATIONS OF AI IN SMART CITIES

The use of artificial intelligence in smart cities can be life changing if implemented in the right spaces. There are multiple zones in cities or in urban development where AI can be used to improve the performance and efficiency of system.

Also Read: What Is Computer Vision: How It Works in Machine Learning and AI?

Advance Security Camera & Surveillance System

AI-enabled cameras and sensors can keep an eye on surroundings to enhance the security level in the city’s neighborhoods. Such cameras can recognize the people and their faces or track the unusual activities done by them at restricted areas.

High-resolution AI security camera system can track movement of all registered vehicles and can monitor crowd density and cleanliness of public spaces round-the-clock. And thanks to the historic data available from different departments of the city, police can predict category and intensity of crimes monitoring all such activities in a particular area.

Vehicle Parking and Traffic Management System

In cities most of the people have their own cars and also there is huge movement of commercial vehicles to transport people as well as goods. So, parking of such vehicles and traffic management is another space where AI can help.

Using the road surface sensors or CCTV cameras incorporated into parking spots allow cities to create a real-time parking and traffic maps, helping drivers to save their time by avoid waiting to find an empty space to move smoothly or be in traffic.

AI-supported traffic sensor systems can use cameras to collect real time data of vehicles on road, and send it to a control centre, which collates the data fed from other points and adjusts the signal timings to ensure smooth flow of vehicles.

Smart transportation also includes public sector, and thanks to AI, there are lots of opportunities for improvement of public transit. And now cab services like Uber are also using AI to give a better riding experience to its customers.

Autonomous Flying Objects for Ariel View Monitoring

AI-enabled drones or autonomous flying similar objects can be used to monitor the inner-city and houses or other concerning areas. The in-built cameras in drones help to provide the real-time visuals of the different locations where humans cannot reach easily or quickly helping administration and security departments to take timely actions.

Autonomous flying drones can track humans, monitor traffic movement and provide the 2D aerial view imagery mapping for better urbanization of cities. It can be used for advance security and surveillance for security departments and crime squad.

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

Face Detection Cameras and Movement for Public Safety

AI in face recognition technology is capable to detect the different people from their faces disclosing their personal identities. AI in security cameras or drones can recognize the human faces and match with the database to trace his identity and authenticate the person entering in the cities, societies and other restricted areas.

Landmarking annotation is the technique used to train the AI-based model in face recognition system. And to make cities smart, such high-level of face detection technology is necessary to make the surroundings more safe and private.

Smart Waste Management and Disposal System

People living in cities produce huge amount of waste that is another challenge for the urban management to find the right way of managing garbage and keep environment clean to maintain the hygiene level in the society. AI-enabled cameras can detect trash thrown on the street and recognize the types of garbage for categorization.

But AI-enabled installing sensors on waste bins can make waste collection more efficient. Authorities can receive notifications when the bins are about to be filled and ensure reducing of operational costs by eliminating unnecessary pickups, providing dynamic collection routes and schedules for optimization of waste management.

Better Governance and Planning Management

While developing the cities or urban townships, AI or machine learning techniques can be used to map land use across time to generate crucial insights using the satellite imagery and aerial view 2D or 3D images of geographical areas.

Also Read: What are the various Types of Data Sets used in Machine Learning?

The algorithms trained through machine learning can analyze satellite images for city planning and development with scope to adjust the formation based on changing data, areas prone to calamities like flooding, earthquake and storms. Such real-time as well as historical data can continuously be monitored to enable the better governance.

Summing-up

AI in smart cities development can play a crucial role in urban planning, development and management with advance security system, traffic monitoring and disposal management to make the societies more secured and livable with access and control of their home and other activities making their living experience more enjoy and comfortable.

And to develop the AI-enabled systems for smart city development, machine learning and AI companies need huge amount of training data for smart cities to train the models like drones, AI security camera and face recognition system to work with accurately and provide the correct information making the smart cities really smart for sustainable growth.

And Cogito can provide such training data sets to train the AI and machine learning system can be used for smart city development. It is actually involved in data labeling job with expertise in image annotation services to annotate the object of interest in images to make it recognizable to machines through computer vision technology.

Also Read: How Computer Vision Can Improve Accuracy of Diagnosis in Medical Imaging Analysis?

Cogito a works with advance data labeling system complying all the data security and protection standards like to make sure provide the high-quality training data sets produced in highly secured and protected environment helping machine learning developers to build a successful and most reliable AI models at low-cost.

Also Read: Why Outsourcing Your Data Annotation Needs to GDPR, CCPA or SOC Type Compliant Companies?

Thanks to AI and machine learning, computer vision technology is getting upgraded with improved version of visualizing making perception through machines reliable. Actually, this is completely related computer based visual processing of objects.

What is Computer Vision in AI and Machine Learning?

Computer vision is the simply the process of perceiving the images and videos available in the digital formats. In Machine Learning (ML) and AI – Computer vision is used to train the model recognize certain patterns and store the data into their artificial memory to utilize the same for predicting the results in real-life use.

The main purpose of using the computer vision technology in ML and AI is to create the a model than can work itself without human intervention. The whole process involves methods of acquiring the data, processing, analyzing and understanding the digital images to utilize the same in the real-world scenario.

How Computer Vision System Work?

You can say computer vision is used for deep learning to analyze the different types of data sets through annotated images showing object of interest in an image. It can recognize the patterns to understand the visual data feeding thousands or millions of images that have been labeled for supervised machine learning algorithms training.

Also Read: How Much Training Data is Required for Machine Learning Algorithms?

This process depends subject to use of various software techniques and algorithms, that are allowing the computers to recognize the patterns in all the elements that relate to those labels and make the model predictions accurately in future. Computer vision can be only utilize only with image processing through machine learning.

What is Difference Between Computer Vision and Image Processing?

Both are part of AI technology used while processing the data and creating a model. The difference between computer vision and image processing is Computer vision helps to gain high-level understanding from images or videos.

For instance, object recognition, which is the process of identifying the type of objects in an image, is a computer vision problem. In computer vision, you receive an image as input and you can produce an image as output or some other type of information.

Whereas, image processing doesn’t need such high level of understanding of image. In fact it is the sub-field of signal processing but also applied to images. For an example, if you have a noisy or blurred images, then under image processing the deblurring or denoising is done to make the object in the image clearly visible to machines.

Image process task involves filtering, noise removal, edge detection, and color processing. In entire processing the you receive an image as input and produce another image as an output that can be used to train the machine through computer vision.

The main difference between computer vision and image processing are the goals (not the methods used). For example, if the goal is to enhance the image quality for later use, which is called image processing. If the goal is to visualize like humans, like object recognition, defect detection or automatic driving, then it is called computer vision.

Application and Role of Computer Vision in Artificial Intelligence

The applied science of computer vision is expanding into multiple fields. From AI development to machine learning, it is playing a significant role in helping the machines identify the different types of objects in their natural environment.

From simple home task to recognizing human faces, detecting the objects in autonomous vehicle, or combating with enemies in war, computer vision the only technology giving an edge to AI-enabled devices to work efficiently.

The application of computer vision in artificial intelligence is becoming unlimited and now expanded into emerging fields like automotive, healthcare, retail, robotics, agriculture, autonomous flaying like drones and manufacturing etc.

Actually, to create the computer vision-based model the labeled data is required for supervised machine learning. And image annotation is the data labeling technique used for creating such labeled images for computer vision.

Cogito is one the companies providing the data annotation service for computer vision providing the image annotation solution for AI and machine learning. Rendering the high-quality training data using the best tools and techniques allowing computer vision to help algorithms train the model perform accurately in real-life use.