Change the way you think about road safety. Stop thinking that car crashes are accidents and start investing in smart technology. It is high time that you introduce AI assistants to accommodate human errors.
Almost all vehicle crashes are caused by human error, which you can avoid by choosing Advanced Driver Assistance Systems (ADAS). These systems use technology to assist the driver during driving, thereby improving drivers.
If you are curious enough to know more about driving assistance and how it works, here is a blog to read!
What is ADAS (Automated Driving Assistance)?
An advanced driver-assistance system is an electronic system in a vehicle that uses advanced technology to assist drivers in driving and parking functions. ADAS primarily focuses on collision avoidance and aims to provide safety features to make driving safer and safer every day.
ADAS-equipped vehicles have an array of advanced sensors that augment the eyes and ears of the human driver for better decision-making.
For example, can a normal human driver see in the dark? No! But RADAR can. Similarly, can he determine if there’s a child behind the car before he puts the car in reverse? No! However, SONAR can! These are the situations where ADAS will step in. ADAS uses cameras and other sensors to check all the directions around the car.
How does ADAS Work?
Building vehicles that can take autonomous actions or offer other assistance services need sensory, cognitive functions (memory, logical thinking, decision-making, and learning) and administrative capabilities.
ADAS equips vehicles with a combination of sensor technologies and AI processing algorithms to sense the environment around the vehicle, process it and then either provide information to the driver or take action. The alerts about the danger to drivers or even taking autonomous steps helps to avoid a car accident.
AI provides computational requirements to the car (or any other vehicle). The car first perceives the surrounding environment through high-resolution, 360-degree surround cameras and lidars and then plans a safe route to the destination based on collected data processing.
Reinforcement learning algorithms are used for multiple levels of task repetition (machine learning) to ensure the greatest level of safety. For algorithm training, vehicles use ADAS data capture and storage computers that are capable of connecting to and capturing information from vehicle cameras and sensors.
As the amount of information being fed into IVI (in-vehicle infotainment) units or telematics systems grows, vehicles will be able to share internal system status along with the location data according to their surroundings, all in real-time.
There are different levels of ADAS. It ranges from simple backup cameras and blind-spot warning sensors to adaptive cruise control, self-parking, and more. Moreover, you can apply it to any vehicle that includes:
- Farming vehicles
- Construction and military vehicles
Object Detection In ADAS
Detecting a pedestrian (or any obstacle) in front of a car is a part of object detection. For each object to be detected, neural network-based techniques have gained momentum. The image classification, detection and recognition no longer have to be a hand-crafted coding exercise, but deep neural networks allow features to be learned automatically from training examples.
Convolutional neural networks (CNN) are currently used for efficiently implementing deep neural networks for ADAS systems. You can deploy state-of-the-art trained object detection neural networks (Yolo versions, SSD etc.) to ADAS systems to detect multiple objects.
Scene Segmentation in ADAS
The goal of scene segmentation is to identify the boundaries between types of objects in the scene. That is to differentiate the road compared to other things in the scene. Using state-of-the-art image segmentation neural networks – one can differentiate CNN for scene segmentation and greatly benefit a car’s navigation.
Passive and Active ADAS Systems
In the passive ADAS system, the computer merely informs the driver of an unsafe condition, and the driver needs to take action to prevent that condition from resulting in an accident. Typical warning methods include:
Sometimes, there is also physical feedback. For example, the vehicle’s steering wheel shakes to alert the driver that the lane he is moving into is occupied by another vehicle (blind spot detection).
While as in the active ADAS system, the vehicle takes direct action. For example, It automatically brakes the car as required to avoid hitting a vehicle ahead or another object (pedestrians, animals, or anything in the lane of travel).
Applications of ADAS
Significant automotive safety improvements with ADAS and AI can avoid or minimize injury during an accident. This technology can physically respond faster than any human driver ever could by analyzing streaming video in real-time to recognize and determine how to react to it.
The key life-saving applications of ADAS are:
Adaptive Cruise Control
Adaptive cruise control is particularly helpful on the highway, where drivers find it difficult to monitor their speed and other cars over a long period of driving.
Glare-Free High Beam and Pixel Light
Glare-Free High Beam and Pixel Light sensors adjust to the darkness and the vehicle’s surroundings without disturbing oncoming traffic.
Here the ADAS system moves a vehicle from a traffic lane into a parking spot to perform parallel, perpendicular, or angle parking. The system does the task on its own without any external human help. Also, data from the parking sensors, when fed to trained AI systems, can additionally benefit the safety of the ADAS systems.
ADAS systems can also display exact traffic data and plan a new route to avoid traffic jams.
Automatic Emergency Braking
Automatic Emergency Braking is a safety system that can identify when a possible collision is about to occur and responds by autonomously activating the brakes to avoid it. It is designed to activate at highway speeds when forward collision warning sensors are able to detect vehicle collisions.
Other application areas include:
- Lane Departure Warning/ Correction
- Traffic Sign Recognition
- Driver Drowsiness Detection
- Night Vision
Vehicles with ADAS systems are also likely to reduce energy use and fuel emissions, both through more efficient operation. These vehicles can now move faster with more safety.
Why Should You Choose Ksolves?
ADAS technology is evolving rapidly! Automobile manufacturers worldwide are investing in it to remove the human error component when operating vehicles of many types. Why do you want to be left behind when you can choose to integrate it into your business? Opt opportunities for growth with Ksolves.
Ksolves is one of the leading companies that offer to build ADAS algorithms using advanced research and providing state-of-the-art AI solutions. We are open to multiple iterations in the implementation process for customisation and improvements. Contact us at email@example.com.
Life-saving systems like ADAS define the true success of AI applications. They not only incorporate the latest interface standards but also run multiple computer- vision based algorithms to support real-time multimedia. The right architecture of sensors, interfaces, and a powerful computer processor integrates all of the data and makes decisions out of it in real-time.
1. What are advanced driving assist systems in cars?
ADAS in cars are intelligent systems that enable cars to take autonomous actions similar to a driver or assist the driver in driving in a variety of ways.
2. Do ADAS- equipped vehicles use sensors?
Yes, ADAS system architecture includes sensors, interfaces, and a powerful computer processor. The most common sensors used are:
- Camera Sensors.
- Radar Sensors.
- Lidar Sensors.
- GPS/GNSS Sensors.
- Ultrasonic/Sonar Sensors.
3. How is the ADAS safety system useful?
ADAS plays an important role in reducing the number of car accidents or reducing the serious impact of those accidents that can’t be avoided.
4. What is the role of AI in ADAS?
AI-driven models, when deployed in the ADAS system, play a crucial role in avoiding accidents on the roads. Pedestrian detection, driver’s dizziness, and scene detection for better navigation are a few examples.