Introduction
We know that blind spot warning systems make driving easy. Their obvious operative principle is target detection. However, most drivers have a common query related to them. How do they work? What bounds help them to judge a vehicle? Well, the algorithms are the answers to all those questions.
Blind spot detection systems have a set of algorithms for aid. These help the system to exactly measure several numerical terms. So, it becomes easier for the system to judge the object if it is a risk. As a result, the system can decide better whether it is crucial to alarm the driver or not.
It is better to know how the blind spot warning systems work. This will help you use the product well. Keep reading to apprehend how algorithms work in a blind spot monitoring system.
What Algorithms Do Blind Spot Warning Systems Use For Functioning?
Any blind spot collision warning system does not use only a single algorithm for its operation. Instead, it uses various algorithms for unlike purposes. Their wide use boosts the detection accuracy of a system. Moreover, with the help of algorithms, blind spot warning systems can measure the speed and distance of the target objects more accurately.
Some of the key algorithms that most blind spot warning systems use are as follows:
- Online boosting Algorithms
- Online learning algorithms
- The rear lamp detection algorithm
- Pedestrian detection system
- Multi obstacle detection cooperative fusion algorithm
- AdTM
- Multi line CCD
- Inverse perspective mapping
- Edge detection algorithm
- Scale invariant feature transform (SIFT)
- Estimation Of Image Entropy
Some of these algorithms are briefly explained below:
Online Boosting Algorithms:
These types of algorithms are mainly for front object detection. This system is a vision-based detection algorithm. So, it uses nonstop image checking to detect hurdles on the front. Unlike other old algorithms, it includes multiple classifiers. These classifiers help the algorithm detect objects in the pictures. This system adapts itself according to the changing traffic conditions. Therefore, we can also refer to it as a reliable detection algorithm.
Moreover, firms use this algorithm mostly in blind spot warning systems, including cameras and screens. It makes the connection easier.
OLA For Blind Spot Warning Systems:
The online learning algorithm has a very exact tactic. Its main objective is to remove any errors in detection while taking a turn. It is also a vision-based algorithm with constant observation.
Rear Lamp Detection:
O’Malley and allies hosted this algorithm for blind spot warning systems. It not only helps in the correct vehicle detection but also helps drivers with several ADAS (advanced driver assistance system).
It automates the features like cruise control and headlight dimming. The algorithm requires numerous conditional constituents to work. These are mostly inside a blind spot detection kit.
- Bayer RGB color filter
- Complementary semiconductor metal oxide
- A Low-cost camera
Pedestrian Detection System:
Liu and Fujimara used this algorithm for exact object detection at night. It helps the blind spot warning systems to have the best night vision. This also helps to raise its overall value and reliability. This algorithm courts the objects on shape-based remarks. The algorithm rejects the objects that are not moving reliably with the background.
Moreover, it examines the error with frame-by-frame arrayed pattern recognition of objects.
Multi Obstacle Detection Cooperative Fusion Algorithm:
It is one of the advanced algorithms. Its complex working modes make it dense to use yet helpful for blind spot warning light systems. These algorithms are mostly present in advanced blind spot warning systems. It contains multiple cohesive cameras and laser scanners. They make it easy for the algorithm to detect numerous obstacles in front of the cars with blind spot warnings. Moreover, with extra cameras and laser scanners, it can guess object height, depth width, and distance with great care.
The detection video feed used by the algorithm is 25 frames per second. Labayrade and allies were the first ones to install the 3D cameras and united laser scanners on blind spot warning systems for multiple object detection. Along with the use of laser scanners, it uses the stereo vision principle for utmost exactness and precision.
AdTM:
It is one of the most basic yet vital algorithms for vehicle discovery. An adaptive template matching algorithm helps the system detect vehicles arriving in blind spots. It uses many sensors like ultrasonic, yaw rate, and steering angle sensors.
Moreover, it helps the blind spot warning systems to find targets through shadow-based classification algorithms. Also, it spots the vehicles according to their traced behavior. For that reason, it builds different levels of risk detection according to the status of vehicles inside the area. It increases the accuracy of detection. Upcoming vehicles have a higher level compared to other objects.
Multi Line CCD:
This algorithm has a major role in finding the height of the object. Moreover, it works on the pixel-based checking principle. So, it helps to revenue a precise and real-time height detection value.
This algorithm is ordinarily present in blind spot warning systems with no camera or screen. So, it does not use camera image inspection. Rather, it contains multi CCD lines. A contrast of their brightness equally forms a 2-dimensional nursing feed. It helps in the detection of vehicles.
Multi line CCD not only spots the objects but shows the live feed to the driver. It uses a heads-up display (HUD) present on the dashboard to inform the driver about the vehicle’s location.
Inverse perspective mapping:
IPM is free of camera tuning or model-based object detection. It helps blind spot warning systems to find whether a vehicle is present in the area or not. It is a general motion-based object detection algorithm that works on both the rear and lane sides of a vehicle.
Edge Detection Algorithm:
As the name states, the function of this algorithm is the same. It detects the edges of an object to judge whether it is present in the alarm area or not. For that reason, it helps the system to work perfectly.
How Important Is Optical Flow Algorithm For Blind Spot Warning Systems?
The optical flow algorithm is somehow the most vital algorithm for blind spot warning systems. The reason is its true and exact detection ability. Using it inside the system ensures error-free detection and alerting.
The optical flow is a vision-based algorithm. Therefore, it depends entirely on the images the camera captures. However, the device contains an analysis of pixel-wise clusters as well. These pixel clusters help decide the height of the target object. So it reduces the chances of false alarms. The optical flow algorithm’s steps in blind spot warning systems are as follows.
- Pattern Recognition Of The Target
- Clusters analysis
- Grouping of clusters
- Basic or pixel-wise clustering
- Optical flow accurate detection
- Image processing.
The optical flow algorithm is really beneficial for blind spot warning systems. The major gains that it supplies to the systems are as follows;
- Robust activity in tunnels
- Better detection accuracy
- Minimizing the chances of false detection
- Prevents the detection of shadows
- Image handling makes the system reliable and precise
The mechanism of action of optical flow detection starts with pattern detection. In this first step, the blind spot warning systems detect a behavioral pattern of the target object. The next step is to examine the clusters. The second last step takes in the basic pixel-wise clustering. In the final step, image inspection starts that aids in correct detection.
How Do Algorithms Help Blind Spot Warning systems In Day And Night Time Detection Of Vehicles?
During the daytime, the natural sunlight makes it easy for the drivers to monitor the road. However, nighttime becomes difficult both for the driver and the detection system. It is due to the vision-based algorithms and some blocking obstacles. Therefore, the day and night detection algorithms are not the same.
We will discuss both of these in detail below.
Day Time Blind Spot Warning Systems Algorithms:
Daytime detection is much easier for vision-based algorithms. Daytime brings some profits with it. There isn’t any extra break in the signals. Moreover, the sunlight makes it easy to detect the estimation of the target object. The steps through which the algorithm helps the daytime detection are as follows:
- Determine The ROI
- Image processing for detection
- Shadow searching
- Determine the boundaries
- Verification Of the vehicle
- Candidate matching
- Behavior judgment
Determine The ROI:
We also name ROI as the detection area. It is the area under which microwaves can easily oscillate. It is also known as the alarm area or alert area. All blind spot warning system has a different ROI system. According to the ISO, the blind spot area is 3 meters to the rear and 3 meters to the lane side of the vehicle.
However, most of the ideal blind spot systems have detection areas far larger than this. Some best blind spot warning systems can have a detection area up to 50m.
Image Processing For Detection:
The next step is processing the image to better detect the target object. The blind spot warning systems cameras are taking the video feed nonstop. Once the algorithm is functional, it starts checking the feed.
The algorithm system is vision-based. Therefore, it uses constantly taken images to monitor the vehicles entering the blind area. Moreover, the algorithm draws a black and white film picture for better detection. Shadow pixels are white. In comparison, the horizontal edges are grey. It helps the blind spot warning systems to separate the environmental elements.
We use an arithmetic expression that helps us find an adaptive threshold. It makes shadow detection better. The expression is as follows.
Shadow Searching:
An easy way to detect the vehicle is to detect its shadow under the sun. It saves the mess of scanning the whole car. Shadow searching not only helps in easy detection but reduces the workload on both drivers and blind spot warning systems. It reduces time consumption and increases the accuracy rate.
Algorithms use several subsections in the whole alarm area. It makes shadow searching much easier. Additionally, it reduces the time limit as well. So, for this process, the system first checks every pixel of every subsection. At last, the data from these together help in vehicle detection.
Determine The Boundaries:
Although detecting shadows is easier and helps in detection. However, some plain situations lead to incorrect detection. In the evening or morning, shadows are not right below the car. Sun makes a certain angle with the vehicle that results in long shadows.
Set reasons can cause a failure in detection. Therefore, the algorithm confirms vehicle boundaries using vertical edges and average intensity.
Verification Of The Vehicle:
Both of the previous methods help a lot in vehicle identification. For that reason, the chances of false detection are near zero. However, confirmation of vehicles is still a crucial step.
The air dam of most vehicles has horizontal edges. So, for the ideal verification, we search for the horizontal edges. Moreover, vertical projections are also taken into concern for this.
Candidate Matching:
The candidate matching car function is for detecting the same car in continual frames. Its prime feature is to match the tracked and detected vehicle and check whether both of these are the same or not.
The first step is to search for the closest vehicle in the next frames. Then with some math expressions, the system processes the height, center row location, and centimeter column. At last, the trajectory forms. Now you can store, inherit or update the vehicle information.
Behaviour Judgment:
There are three conditions for the behavioral judgment of a vehicle.
- Relative backing
- Relative approaching
- Relative static
These conditions help identify how big of a risk the vehicle is posing to the driver. Therefore it allows the system to judge it as a risk and alarm the driver.
Night Time Detection Of Vehicle With Algorithms:
The steps and mechanism of nighttime detection are a bit different than day time. Its biggest reason is the light and sun. Unlike day time, algorithms of night time use lamp lights to detect and determine the variables of the target. The steps through which it detects the target are as follows.
- ROI Determination
- Lamp Extraction And Verification
- Lamp Tracking
- Behavioral Judgment
ROI Determination:
The ROI for daytime is not fit for the night. The reason is that there is no sun. For that reason, systems use changed algorithmic methods to detect the vehicles. Mostly the car lamps simplify the ROI at night.
Lamp Extraction And Verification:
As said earlier, there is no light for image handling. So, lamp lights come in handy. These lights are the targets that the system searches for and detect. Moreover, the algorithm reflects the 1% brightest part of the ROI as bright objects. Therefore, it filters them out. These primarily include street lamps and reflection of car lamps.
Lamp Tracking:
Lamp tracking is only possible in tracking mode. Its main purpose is to make connections across different lamps in serial screen frames. It has exact working conditions. Moreover, it can successfully compare the object height in present and previous frames.
Behavior Judgment For Night Time:
For lamp behavior judgment, column information is much more important. The reason is the movement of lamps. Lamps move either right to left or left to right. Therefore, the judgment input parameter changes to the column index.
What Are The Factors Of A Car Detection That Algorithms Determine?
Algorithms of blind spot warning systems perform the most basic and vital functions. Any error in the algorithm results in complete failure of detection. Moreover, it can also lead to false detection. So, to ensure accurate and precise detection, systems use exceptional algorithms and techniques.
Every algorithm has a specific field of expertise. Therefore, every algorithm is unmatchable in a specific field of detection. Some of the detection factors that algorithms cover are as follows.
- Height
- Distance
- Shadows
- Relative speed
- Pattern Of Movement
- Behavior of target
These are some of the factors algorithms determine. However, keep in mind that a single algorithm cannot cover all these determinants. Different conditions demand different algorithms. Therefore, choose the algorithms that suit your driving routine.
Conclusion:
All blind spot warning systems have to detect the target object for proper functioning. However, algorithms take the concept of detection to a new level. Their mathematical expressions help measure the height and behavior of the target object. It helps in accurate, reliable, and swift detection of the target.
However, not every blind side warning system is universal. Try to choose the blind spot system with those algorithms that could work 24/7 with the same capability.
