Have you ever think about what mechanism of AI driving uses in self-driving cars and how this technology works? We all know that Artificial intelligence keeps on evolving and transforming almost every industry and so does the transportation industry too. Today it’s a revolution to have AI-powered features in the vehicles, especially in cars and moving towards the compulsory and must have feature.
Many car companies started introducing AI technology in their latest car models with some basic and some advanced features, from driving assistance to technical assessment.
Here we are going to discuss how AI can help in driving cars and keep the road safer for everyone and what mechanism of AI in self-driving car works behind? Along with some negative and positives aspects. All in detail.
How does AI work in Self-driving Cars
As we all know that artificial intelligence and machine learning technology are occupying in every field to autonomous the system from research, analytics to self-driving cars.
Mechanism Behind
To integrate the car to autonomous includes numerous sensors and applications to interconnect and work along. It’s a centralized electronic control unit that works to automate by judging the hurdles and deploy machine learning to process the required action. It seems to be a complex mechanism, but don’t worry we are going to share it all in simple language for you to understand how self-driving cars use AI.
Get latest news about artificial intelligence at AI Updates News
There are some applications and systems that work along to execute safe driving includes:
- Cameras
- Sensors
- Radars
- Lidars
How do self-driving AI cars work
The Supervision algorithm works on multiple cameras (External and internal). Numbers of cameras are integrated to the car that catch the images of objects from front, rear, sides and bottom. Based on the images it passes the information to the sensors to identify the risk factor and work accordingly.
If a car is set on a self-driving mode then its sensors recognize the distance from the objects and control the speed to make a proper distance. Its unsupervised algorithm senses to identify the patterns based on the data set and behaves to the situation as per the environment.
This entire system of machine learning and self-driving works on 5 mechanisms from recognition of an object to action execution, these are:
1. Object Detection
It is the first and very important step of the self-driving algorithm. It works on the basis of cameras, radars and sensors. All these three stay interconnected and responsible to detect the object, its distance, shape and mobility. It allows the image sampling of an object and passes to the next algorithm to work on.
2. Object Recognition
Once the image sampling is done based on the distance, shape, pattern and mobility, now the recognition algorithm starts working. It extracts the pattern and matches the sample image from its data set. This stage finishes so quickly and passes the information about the image after recognizing.
3. Object Classification
Once the image gets identified, the machine learning algorithm starts giving predictions based on the classified pattern. It works on ADAS that identifies the lines (whether straight or circular) and side edges.
The support vector machines (SVM) are commonly used in ADAS to recognize and identify an image that includes histograms of oriented gradients (HOG) and principal component analysis (PCA). Apart from these KNN (K nearest neighbor) and BDR (Bayes decision rule) works along to classify the recognized image for action prediction.
4. Object Judging and Prediction
This phase works on DMA (Decision Matrix Algorithm). Once the image gets captured and recognized and analysis gets done based on the different patterns and parameters. The relationship gets transformed from the environment based on the data set and values. The prediction is made according to the movement or mobility of the object.
5. Action and Execution
Once the prediction is made by an independent algorithmic model, it’s time for an action. The GDM (Gradient Boosting) and AdaBoosting algorithms execute the actions like to apply the brake or to slow down the speed. These two algorithms are trained to make an action with reduced possibility of an error.
Frequently Asked Questions
What if images captured are not clear?
The algorithms that handle this type of situation are K-means and MNN (Multi-task Neural Network). These algorithms are trained for identifying the image if it is blurred, incomplete or unrecognizable. This can happen by the low resolution of an image that’s been captured, the camera is not clear or any other reason.
In this situation these two algorithms start working to lower the error. Based on a blurred or incomplete image, it finds the similar pattern of images and matches them with it. After getting the perfect match based on the trained data set. These help the machine learning algorithm of self-driving cars to make a decision and action accordingly.
How can AI improve Driving?
Though self-driving cars powered by AI are in the initial stage right now but are bringing a quick revolution in the automotive industry. It helps to assess the nearby objects and navigates to control the vehicle. Maintains the distance from other vehicles by applying brakes or lowering the speed. This helps to keep roads safer as many times car drivers drive the car so harshly. They unnecessary accelerate it fast though there is a rush right forward and then apply the brakes too near to the next objects.
Many times it leads to an accident if they could not control it at the end moment. Here self-driving cars plays as a safer drivers as it keeps the distance from other vehicles.
Are self-driving cars 100% safe?
Self-driving cars work on the basis of its algorithms by getting the responses captured by sensors, cameras and radars. If any of the application or software malfunctions, will lead to a vehicle out of control. But it can safer 99.999816%.
