Assignment on Artificial Intelligence - Pedestrian Detection for the Self-driving Cars

Running head: ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE
Name of student
Name of university
Author’s note:

1
ARTIFICIAL INTELLIGENCE
Pedestrian detection for the self-driving cars
The intelligent, self-driving and self-guided systems in the vehicles have been
introduced in the modern world. The cars are not discovered in the streets yet but this
technology is being constantly detected and altered for making it suitable to introduce in the
roads (Litman, 2017). The automatic system in the cars mainly utilises the algorithms of
computer vision for detecting as well as the predicting the activity for all the other traffic
participants that includes the other vehicles as well as pedestrian and any other animals. Main
intention of the approach of machine learning in the vehicle system is probability of
predicting the dangerous situations on roads prior occurring (Bansal & Kockelman, 2017).
The main critical benefit that would be provided by the system to the cars is the
distinguishing pedestrians as well as the vehicles in the motion for allowing the undertaking
of safer as well as the smarter decisions in driving. The approaches of computer vision on the
solutions of neural networks permits the detection of variety of the objects with various
accuracy (Koopman & Wagner, 2017). The most common topologies of the neural network
for the pedestrian as well as the vehicle are the SSD, MobileNet, and GoogleNet. All the
modern solution, which localises the pedestrians with significantly high precision.
The main limitation of the pedestrian detection by the self-driving cars is that not
always the cameras would be able to detect the pedestrians in the busy backgrounds and
during the situations when the vehicle is travelling at significantly high speed (Koopman &
Wagner, 2016). The technologies that are integrated in the cars for allowing the detection of
pedestrians are might face technical issues that could lead to malfunction and it is not able to
detect the pedestrians. The algorithms that are designed for detecting pedestrians might not
detect the appropriate pedestrians in the busy crowds and it could lead to loss of human life.
The algorithms could be modified by the malicious attackers for taking the life of any

2
ARTIFICIAL INTELLIGENCE
individual and the cars could be utilised for illegal purposes as there is no accountability of
who is driving the car (Kalra & Paddock, 2016).
Road and lane detection for the self-driving cars
The road and lane detection is considered among the preliminary features that are
required to be implemented in the autonomous vehicles. This feature mainly works with the
theory that the lanes are highlighted by the algorithm as there is any movement of the
vehicles and it is displayed on the screen in the car. The detection of lane lines in presently
considered as the crucial task. It offers the lateral bounds on movement of the vehicle and
provides the idea regarding the curvature of any road as well as the amount of deviation that
is encountered by the vehicle from centre of lane. The road and lane detection in the self-
driving cars are mainly based on the camera images (McAllister et al., 2017). Then the
techniques of computer vision is applied for processing the images and provide the lane
markings as output to the driver. The simplest lane and road detection is implemented by the
method of color thresholding. Using this method, the applying of threshold is done over value
of the color channels. As an example, for detecting the white markings in the lanes, the most
appropriate threshold for the RGB color channel could be implemented in the algorithms for
allowing it to detect the lanes and roads. For improving the road and lane detection in the
self-driving cars, the recent modification that has been done is the introduction of line
detection as well as the edge detection within the images. The pipeline for implementing it
includes the techniques of computer vision like the Gaussian filtering, image greying along
with the Canny Edge Detection and then finally utilising the Hough transform for gaining the
number of the candidate line segments.
The main limitation of the road and lane detection in the self-driving cars is the
technical issues in the equipment of the cars that leads to the incorrect detection of road or