Accident Avoidance in Driverless Car using Deep
Learning Algorithms
Mrs. Babitha.S1, BG Tejas2, Abheer Patil3, BS Jayanth4
C Shreyas5
1
babithagi@gmail.com, Associate Professor
2
abheerpatil19ec01@gmail.com,3bgtejas2001@gmail.com,4jayanth.bshi
vashankara@gmail.com, 5shreyas.cvss@gmail.com
DON BOSCO INSTITUTE OF TECHNOLOGY, BANGALORE, INDIA
Abstract: This abstract presents a ground breaking paradigm for accident mitigation in autonomous
vehicles through the utilization of Raspberry Pi and deep learning methodologies. By synergistically
amalgamating cost-effective hardware and sophisticated AI techniques, the system empowers real-time
object detection and recognition, thereby augmenting the vehicle's capacity to pre-emptively avert
collisions. The efficacy of this system is meticulously assessed through meticulous simulations and
rigorous real-world experimentation, thereby substantiating its efficacy in bolstering safety measures.
Noteworthy challenges encompassing the procurement of comprehensive training datasets,
computationally intensive requirements, and real-time optimization are duly acknowledged.
Keywords: Autonomous Vehicle, Artificial Intelligence, Radar, Sensor Fusion, Self-driving cars
1. Introduction
Self-operating conveyances, commonly referred to as autonomous vehicles, represent a paradigmatic leap
in transportation technology, embodying the pinnacle of robotic ingenuity [1]. These ingenious machines
are meticulously engineered to traverse the vast expanse between destinations without the need for
human intervention. Armed with cutting-edge control systems and imbued with the power of artificial
intelligence, they possess the extraordinary ability to discern and navigate their surroundings with
impeccable precision.
Of particular significance is their remarkable capacity to discriminate and delineate diverse vehicular
entities that populate the roadways. Through the meticulous analysis of sensor-derived data, these
sentient contrivances are endowed with the cognitive prowess to discern and categorize various vehicular
archetypes, empowering them to execute informed judgments and navigate complex traffic scenarios
with unwavering acuity.
The potential advantages of autonomous vehicles are both manifold and profound, encompassing a
comprehensive array of realms. Among their illustrious merits lie the prospect of ameliorated cost-
efficiency in mobility and infrastructure, heightened road safety, amplified accessibility for diverse
demographics, elevated consumer satisfaction, and a notable decline in criminal activities. Paramount
among these advantages is the substantial reduction in traffic-related collisions, concomitant injuries, and
the resulting financial burdens, inclusive of the mitigated need for exorbitant insurance premiums.
Self-driving cars hold the promise to revolutionize traffic flow dynamics, catering to the nuanced
mobility requirements of the vulnerable demographics such as children, the elderly, and individuals with
disabilities. By alleviating human operators from mundane driving and navigational chores, they liberate
valuable time and enhance fuel consumption efficiency [3]. Furthermore, the advent of autonomous
vehicles ushers in a transformative era wherein the exigent demand for expansive parking spaces
dwindles significantly. The disruptive potential of this nascent technology becomes all the more
conspicuous as it paves the way for pioneering business models rooted in transportation-as-a-service,
particularly within the burgeoning realm of the sharing economy.
Nonetheless, notwithstanding the myriad of potential boons entailed by heightened vehicular automation,
an assemblage of unresolved quandaries arises, casting a shadow over the utopian horizon. Foremost
among these quandaries are the formidable matters of safety assurance, intricate technological
conundrums, liability disputes, apprehension surrounding relinquishing control, public anxiety regarding
the safety of driverless cars, the imperative need for a comprehensive legal framework, and the
establishment of stringent governmental regulations. Additionally, the specter of increased suburban
sprawl looms ominously, precipitated by the advent of cost-effective and time-efficient travel facilitated
by autonomous vehicles [6]. These multifarious challenges necessitate judicious deliberation and
proactive mitigation to ensure that the potent capabilities of self-driving cars are harnessed while
effectively addressing and mitigating any potential drawbacks.
2. Proposed System
The present endeavour strives to construct an ambitious prototype of an autonomous vehicle, harnessing
the potential of monocular vision, with Raspberry Pi serving as the vanguard processing chip. This
visionary undertaking encompasses the integration of a high-definition camera in conjunction with an
ultrasonic sensor, deftly capturing and relaying indispensable real-world data to the vehicle's cognitive
framework. Empowered by its inherent intellect, this cutting-edge automotive marvel demonstrates an
exceptional aptitude for navigating predetermined destinations with meticulous precision, deftly
circumventing the perils of human fallibility. To accomplish this remarkable feat, a myriad of well-
established algorithms, including but not limited to lane detection and obstacle detection, harmoniously
converge, providing the requisite control mechanisms for the vehicle's operation.
At the core of this pioneering autonomous car prototype lies a neural network model, which, when
deployed on a computer system, unveils its formidable prowess in predictive analytics. Leveraging a
plethora of input images procured from the vehicle's intricate sensory apparatus, this neural network
traverses intricate layers of computation, ultimately yielding astute predictions regarding steering
behavior. This comprehensive system operates in perfect concert, synergistically amalgamating the
potent forces of monocular vision, cutting-edge computational hardware, and sophisticated neural
networks, thereby engendering a superlative vehicular embodiment that deftly maneuvers through its
surroundings with unparalleled finesse and sagacity. A neural network model runs on computer and
makes predictions for steering based on input images.
Control Flow:
Environment Camera Raspberry Car
Pi
The car is
The processor
connected
receives the
The The camera data from
with DC
condition of captures the camera, motors
the road, video of the processes it through
lane and road and takes a which the
other sends the particular motion is
factors are data to the decision achieved
considered. processor. based on based on the
deep learning
decision
algorithms.
obtained
from the
Fig2: Control flow Diagram for driverless car
The proposed framework entails the utilization of a sophisticated arrangement whereby an image is
acquired through the utilization of the Pi cam securely affixed to the Raspberry Pi integrated within the
vehicle. Establishing a seamless connection, both the Raspberry Pi and the laptop are interconnected
within the same network, facilitating the transmission of the captured image from the Raspberry Pi to the
Neural Network. Prior to its analysis, the image undergoes a transformation, transitioning to a grayscale
format, thus priming it for subsequent processing within the Neural Network. Following the intricate
computational procedures, the model generates one of four potential outputs, specifically denoting
leftward, rightward, forward, or cessation of motion. Upon the conclusive prediction, a corresponding
signal is seamlessly initiated within the Raspberry Pi, instigating the vehicle's deliberate movement in the
prescribed direction, facilitated by its formidable controller. The hardware components constituting this
pioneering project encompass the Raspberry Pi itself, the Pi camera, the L293D motor driver, 12V DC
motors, and the requisite power supply.
3. Hardware implementation
Hardware model:
Fig 3: Hardware connection.
Hardware connection consists of:
Raspberry Pi: diminutive single board computer, boasts a commendable processing capacity, with clock
speeds spanning from 700 MHz to 1.2 GHz for the Pi 4B iteration. In terms of onboard memory, this
remarkable device accommodates an extensive range, encompassing RAM capacities that stretch from
1GB to an impressive 8GB. Its manifold functionalities encompass support for up to four Universal
Serial Bus (USB) ports, seamlessly integrated with the inclusion of a High-Definition Multimedia
Interface (HDMI) port.
Pi Camera: Emanating a captivating visual panorama, it effortlessly captures still images of remarkable
width, boasting a stunning resolution of 2592x1944 pixels. In the realm of video, this camera module,
specifically Camera module v1, dazzles with its ability to record videos of unparalleled quality, offering a
cinematic 1080p resolution at a smooth frame rate of 30 frames per second.
L293D motor driver: a sleek 16-pin configuration, replete with eight pins on each side, this versatile IC
affords exquisite control over the connected motor. L293D to effortlessly govern up to two DC motors,
thereby amplifying its practicality and efficiency. Central to its operation, the L293D encompasses two
distinct H-bridge circuits, representing the epitome of simplicity in altering the polarity across the load
interconnected to it. In this paradigm, two 12V DC motors form an integral part of the motor driver,
wherein a meticulous orchestration of operations ensues, facilitating bespoke functionalities tailored to
the specific requirements at hand.
DC motor: A direct current (DC) motor stands as a remarkable electro-mechanical apparatus,
proficiently effectuating the conversion of electrical energy into mechanical work. Operating under the
premise of harnessing direct current as the input electrical energy, this ingenious motor seamlessly
transforms the electrical impulses into precise and controlled rotational motion. In this particular
instantiation, a notable inclusion comprises the utilization of two 12V geared DC motors, distinguished
by their inherent ability to exhibit exceptional torque and efficiency. Precisely engineered to revolve at a
predetermined speed, these motors operate at an impressive rotational velocity of 30 revolutions per
minute (rpm), exemplifying a harmonious synthesis of electrical energy transformation and mechanical
motion generation.
Voltage regulator: A voltage regulator, a critical component within electrical systems, epitomizes a
sophisticated device that governs and maintains a stable and precise output voltage, regardless of
fluctuations in the input voltage or load conditions.
Ultrasonic Sensor: The HC-SR04 ultrasonic sensor uses SONAR to calculate an object's distance. It
provides exceptional non-contact range detection from 2 cm to 400 cm or 1 inch to 13 feet in an easy-
touse compact with high accuracy and consistent readings. The sensor can detect liquid, coolant, or any
other hard substance and is unaffected by sunshine or dark materials. It includes an ultrasonic transmitter
and receiver module in its entirety.
4. Results
In this section we discuss the hardware and software results of the project carried
out.
Sign detection
The underlying complexity of sign detection arises from the intricate computational procedures
involved, encompassing intricate neural network architectures, intricate feature extraction
methodologies, and intricate decision-making algorithms. Through the fusion of machine learning and
computer vision techniques, sign detection systems showcase a remarkable capacity to discern and
interpret a wide range of signs, including traffic signs, road signs, and informational signs, among
others.
Table:1 Test results of sign detection
Sl # Test Unit testing
Case
Name of Vehicle control depending on signal condition
Test
Item Vehicle start and stop at signal
being
tested
Sample Capture image and send density count to hardware
Input
Expecte Control of vehicle movement
d output
Actual Vehicle start and stop operation achieved successfully
output
Remarks Pass.
Fig 4: Sign Detection
Lane detection
Lane detection, an intricate computer vision process, pertains to the identification and tracking of lane
boundaries within a visual scene. Leveraging sophisticated image processing techniques and advanced
algorithms, lane detection systems meticulously analyze digital images or video frames, discerning and
delineating the distinct patterns, textures, and geometrical structures associated with lanes on roadways.
Fig 5: Lane Detection
Table 2: Test results of lane detection
Sl # Test Case Unit testing
Name of Test Detection of Lane
Items being Tested for uploading different images
tested
Sample Input Upload Sample image
Expected Should detect the lane
output
Actual output Lane detection Successful
Remarks Pass.
Object detection
The implications of robust object detection systems are profound and multifaceted. In the realm of
autonomous driving, precise object detection enables vehicles to identify and track potential hazards, thus
enhancing safety and facilitating intelligent decision-making. In the domain of surveillance and security,
object detection systems aid in the identification of suspicious activities or objects, contributing to
proactive threat mitigation.
Fig 6: Object detection
Table 3: Object detection
Sl # Test Case Unit testing
Name of Test Object Detection
Items being Detection of object in front of the vehicle
tested
Sample Input Tested for objects placed in front of the camera
Expected Should detect the object and classify it
output
Actual output Object detection passed
Remarks Pass
Pothole detection:
Pothole detection in driverless cars involves employing sophisticated sensor systems with advanced
algorithms for feature extraction and pattern recognition. These algorithms utilize complex
mathematical models and machine learning techniques to accurately identify and classify potholes
based on distinctive visual attributes. This information is then used to make precise adjustments to the
driving trajectory, ensuring enhanced safety and comfort for passengers.
Fig 8: Pothole detection
Table 4: Pothole detection
Sl # Test Case Unit testing
Name of Test Pothole detection
Items being Detection of pothole in front of the vehicle
tested
Sample Input Tested for potholes placed in front of the camera
Expected Should detect the pothole and avoid it
output
Actual output Pothole detection passed
Remarks Pass
Fig 7: Working Model of driverless car
The interplay of these intricate components results in a driverless car that can intelligently and
autonomously navigate its intended path, adhering to traffic rules, avoiding collisions, and reaching its
destination with utmost efficiency and safety.
This remarkable technological achievement represents the culmination of relentless research,
development, and integration of complex systems. The driverless car stands as a testament to the
seamless fusion of advanced sensing technologies, sophisticated artificial intelligence algorithms, and
precise control mechanisms, promising a future of enhanced mobility, increased road safety, and
transformative transportation experiences.
Lane detection, pothole detection and avoidance, sign detection, and object detection are integral
components of autonomous vehicle systems aimed at preventing accidents. These functionalities
undergo rigorous testing procedures employing fundamental principles to guarantee their dependability
and efficacy. Lane detection testing entails comprehensive data acquisition encompassing diverse road
and lighting conditions, as well as lane configurations. The data is subsequently trained and the results
will be evaluated accordingly.
Conclusion
In summation, autonomous vehicles have materialized as an epochal technological innovation, poised to
exert a transformative influence on the realm of transportation. Their intricate proficiency in perceiving
their surroundings, engaging in astute decision-making, and seamlessly executing precise control
maneuvers heralds a paradigmatic shift, capable of engendering profound ramifications. Foremost
among these is the potential for considerably elevated road safety, optimized traffic flow, and amplified
inclusivity, thereby affording individuals with limited mobility newfound freedom of mobility.
However, while substantial advancements have been attained, formidable obstacles persist,
encompassing the formulation of comprehensive regulatory frameworks, garnering societal acceptance,
and tackling thorny ethical dilemmas. Nevertheless, the trajectory of self-driving cars portends a highly
promising trajectory. The ceaseless march of progress in artificial intelligence, sensor technologies, and
interconnectedness shall undoubtedly refine and augment their capabilities, catalyzing their widespread
adoption.
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