A REVIEW ON MACHINE LEARNING IN ADAS

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072
© 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 685
A REVIEW ON MACHINE LEARNING IN ADAS
Vijayalaxmi H1, Aakash A Kumar2,, Arpith M3 ,,Prajwal U4 ,,Vismay UR5
1Assisstant professor, Dept. of Computer Science Engineering, REVA University, Karnataka, India.
2345 UG Student, Dept. of Computer Science Engineering, REVA University, Karnataka, India.
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Abstract - A Review on Machine Learning in Advanced
Driver Assistance Systems (ADAS)" provides a
comprehensive overview of the current state of research in
the intersection of machine learning and automotive
technology. This review delves into the crucial role that
machine learning techniques play in enhancing the
capabilities of Advanced Driver Assistance Systems, which
are pivotal for improving vehicle safety and automation.
The abstract summarizes key advancements in machine
learning algorithms and models applied to ADAS,
addressing challenges such as object detection, recognition,
and decision-making processes in real-time driving
scenarios. Additionally, the abstract highlights the evolving
landscape of data sources, including sensor fusion and the
integration of deep learning methodologies, contributing to
the overall efficacy of ADAS. By synthesizing existing
literature, this review aims to provide valuable insights into
the recent developments, achievements, and potential future
directions within the realm of machine learning in ADAS,
fostering a deeper understanding of this critical
technological domain.
Key Words: Object Detection, Lane Detection and
Recognition
1.INTRODUCTION
The integration of machine learning (ML) techniques in
Advanced Driver Assistance Systems (ADAS) has emerged
as a transformative force in the automotive industry,
revolutionizing the landscape of vehicle safety and
automation. As vehicles become increasingly
sophisticated, the reliance on intelligent systems capable
of interpreting complex driving scenarios and making
swift decisions is paramount. This introduction sets the
stage for a comprehensive review that explores the
synergies between machine learning and ADAS. The
discourse navigates through the intricate web of
algorithms, models, and applications within this domain,
shedding light on the advancements and challenges that
define the intersection of machine learning and
automotive technology. By delving into the intricate
details of ML applications in ADAS, this review aims to
provide a holistic understanding of the current state of the
field, offering valuable insights into the ongoing evolution
and prospects of this dynamic and rapidly evolving
technological landscape.
2.LITREATURE REVIEW
[1] An Intelligent Driving Assistance System Based on
Lightweight Deep Learning Model : Published in year 2022
The rise of Advanced Driver Assistance Systems (ADAS) is
discussed, emphasizing the use of cameras and radars for
environment sensing. The study focuses on the proposed
system's use of optical cameras for object detection,
utilizing colour features and machine learning techniques.
The paper discusses various object detection methods,
machine learning technology, and the application of deep
learning in the development of the intelligent driving
assistance system. The contributions of the study include
the collection of comprehensive datasets, the development
of a lightweight deep learning model for object
recognition, distance estimation method for collision
warnings, and situation recognition method for reminding
drivers of necessary actions.
Brief Insight: The paper discusses the increasing demand
for accuracy and efficiency in deep learning models due to
technological advancements. It highlights two main
approaches for training lightweight deep learning models:
compressing pre-training network models and training
lightweight network models directly. It specifically
mentions Mobile Net and MobileNetV2 as examples of
models that use depth-wise convolution in training
lightweight network models. The study then applies the
lightweight deep learning model for vehicle detection.
Overall, the section emphasizes the need for lightweight
deep learning models and provides insights into different
approaches and technologies used in this area.
Concluding, The research paper proposes a computational
efficient solution for driver assistance systems that can be
implemented on consumer embedded platforms for
Advanced Driver Assistance Systems (ADAS). The
proposed system includes functionalities such as collision
alarm and driver reminding service. Collision alarm is
implemented using a lightweight Convolutional Neural
Network (CNN) model and distance estimation method,
while the driver reminding service is implemented
through situation recognition method. Experimental
results show that the proposed methods and the adopted
model achieve sufficient computational efficiency and
performance, particularly with the smaller size of the CNN
model, making it more likely to be processed with limited
computing resources in embedded systems. In conclusion,

the paper offers an efficient solution for driver assistance
systems suitable for implementation on consumer
embedded platforms for ADAS.
[2] This research paper introduces an innovative AI-based
model for automatic lane line detection using video
footage from a camera strategically positioned inside a
vehicle. The camera, located behind the windshield,
captures real-time road scenes, and the model follows a
three-step methodology encompassing pre-processing,
area selection, and road line determination. Pre-
processing involves noise reduction, greyscale conversion,
and binarization of RGB images. A polygonal region in
front of the vehicle is chosen as the area of interest, with
the final stage employing canny edge detection and the
Hough transform to accurately identify major road lines.
The model demonstrates an impressive accuracy rate of
approximately 99.9%, showcasing its efficacy for self-
driving vehicles. Emphasizing image processing and road
detection, this project holds significant potential for future
applications in autonomous driving, specifically focusing
on white markings on roads. However, it acknowledges
limitations, as it may not be universally feasible for all
types of roads.
The model proposed in this paper aligns with recent
advancements in the field, integrating advanced
preprocessing techniques to enhance the clarity and
distinctiveness of lane markings within captured video
footage. By strategically positioning the camera behind the
windshield to provide a central view of the road, the
system optimizes detection while minimizing blind spots.
The utilization of canny edge detection and the Hough
transform within predefined regions of interest represents
a significant advancement in lane detection accuracy and
reliability. These techniques enable the model to discern
subtle variations in lane markings and distinguish them
from other features, facilitating precise localization and
trajectory prediction for autonomous vehicles.
[3] To enrich the literature review section of the paper, it's
essential to delve into recent advancements in machine
learning (ML) tailored for Advanced Driver Assistance
Systems (ADAS). Recent developments in ML techniques,
particularly those specifically adapted for ADAS
applications, have garnered significant attention.
Innovations in algorithms, models, and methodologies
have emerged, showcasing promising strides in enhancing
ADAS capabilities. From deep learning architectures to
reinforcement learning algorithms and unsupervised
learning methods, the landscape of ML in ADAS has
witnessed notable progress. These advancements have
enabled more robust and efficient systems for collision
avoidance, lane detection, and driver monitoring, among
other critical components of ADAS.
A comparative analysis of various ML techniques
employed in ADAS applications reveals insights into their
performance, efficiency, and scalability across different
domains. Supervised, unsupervised, reinforcement, and
deep learning approaches have been rigorously evaluated
in real-world scenarios, offering distinct advantages and
challenges. Understanding the strengths and limitations of
each technique is crucial for optimizing ADAS functionality
and ensuring safe and reliable operation under diverse
driving conditions.
Despite the remarkable progress made in integrating ML
into ADAS, several challenges and limitations persist. Data
privacy concerns, model interpretability, and real-time
processing requirements remain key areas of concern.
Additionally, the adaptability of ML algorithms to dynamic
driving environments poses significant technical hurdles.
Addressing these challenges necessitates ongoing research
efforts aimed at enhancing algorithmic robustness,
scalability, and interpretability in real-world applications.
Case studies and practical applications serve as invaluable
examples of ML's impact on ADAS systems. Industry best
practices, success stories, and challenges encountered
during implementation provide valuable insights into the
practical implications of ML in automotive safety. By
highlighting real-world applications, researchers and
practitioners can glean valuable lessons and insights into
optimizing ADAS performance and user experience.
Looking ahead, future directions and research
opportunities abound in the realm of ML for ADAS.
Emerging trends such as autonomous driving, human-
machine interaction, and multi-modal sensor fusion
present exciting avenues for exploration. Exploring these
frontiers requires interdisciplinary collaboration and
innovative research methodologies to unlock the full
potential of ML in shaping the future of automotive safety.
Ethical and regulatory considerations loom large in the
deployment of ML in ADAS. Addressing issues related to
algorithmic bias, accountability, transparency, and
regulatory compliance is paramount. Developing
frameworks for responsible AI development and
deployment is essential to foster trust and confidence in
ADAS technologies and ensure their ethical and societal
implications are carefully considered.
Incorporating these diverse content areas into the
literature review offers a comprehensive understanding of
the current state-of-the-art in ML for ADAS. By
synthesizing recent advancements, comparative analyses,
practical applications, future directions, and ethical
considerations, researchers can navigate the complex
landscape of ML in ADAS and drive innovation towards
safer and more intelligent automotive systems.

[4] Lane detection represents a critical aspect of Advanced
Driver Assistance Systems (ADAS) and autonomous
vehicles, ensuring safe navigation within traffic lanes.
Traditional methods in lane detection have historically
relied on geometric modelling and hand-crafted
techniques. These approaches typically involve a series of
steps, including image preprocessing, feature extraction,
lane model fitting, and line tracking. Techniques such as
Inverse Perspective Mapping (IPM)/Perspective
Transform, edge detection-based methods, and
morphological operators have been utilized to extract
features and identify lane boundaries. While effective,
traditional methods often entail a labour-intensive and
time-consuming process.
In recent years, the landscape of lane detection has been
reshaped by the integration of Artificial Intelligence (AI)
techniques, particularly Deep Learning (DL) and Machine
Learning (ML). DL has garnered attention for its ability to
handle complex tasks and process large volumes of data
efficiently. DL approaches utilize neural networks to learn
features directly from raw data, eliminating the need for
hand-crafted feature extraction. ML algorithms such as
Bayesian Classifier, Haar Cascades, Extreme Learning
Machine (ELM), Support Vector Machine (SVM), and
Artificial Neural Network (ANN) have also been applied in
lane detection, showcasing the diversity of approaches
within the field.
The integration of DL with other ML techniques and
traditional methodologies has emerged as a prevalent
trend in recent research. Some studies have explored
stand-alone DL implementations, leveraging the power of
deep neural networks to accurately detect lane markings.
Others have combined DL with geometric modelling or
other ML algorithms to enhance performance under
challenging conditions. Notably, attention mechanisms
within DL have shown promise in improving detection
accuracy, particularly in scenarios with complex road
conditions or occlusions.
Despite the advancements in AI-driven approaches,
several challenges persist in lane detection. The diverse
nature of lane markings, varying road conditions, and the
inherent slenderness of lanes pose significant obstacles to
accurate detection. Additionally, traditional methods often
struggle to adapt to dynamic environments and may
require extensive parameter tuning for optimal
performance.
The literature review underscores the evolving landscape
of lane detection, driven by advancements in AI and ML
technologies. By synthesizing existing research, this
review provides valuable insights into current
methodologies, challenges, and opportunities in lane
detection. It serves as a foundation for future research
endeavours, guiding the development of more accurate,
efficient, and robust lane detection systems for
autonomous driving applications.
3.SCOPE OF WORK:
The primary focus of this research project is centered on
enhancing road safety through the development and
implementation of an Advanced Driver Assistance System
(ADAS). The overarching goal is to mitigate the risk of
accidents and improve overall traffic safety by leveraging
cutting-edge computer vision and control techniques. The
project's scope encompasses various crucial aspects,
including accurate lane detection to ensure proper vehicle
positioning within marked lanes, real-time calculation of
lane curvature for early identification of potential hazards,
and robust obstacle detection.
4.METHODOLOGY
STEP A - System Calibration and Lane Detection:
1.Perform camera calibration using chessboard images for
intrinsic and extrinsic parameters.
2.Employ image processing techniques, including Sobel
operators and thresholding, to enhance lane visibility.
3.Quantify defects by measuring lane curvature and
vehicle offset from the lane center.
STEP B - Obstacle Detection and Knowledge Acquisition:
1.Implement Haar cascades-based object detection for
identifying cars, bikes, buses, and pedestrians.
2.Review literature on ADAS, computer vision, and
obstacle detection for insights into underlying reasons for
defects.
STEP C - Steering Control and Maintenance Suggestions:
1.Develop a servo control strategy for steering commands
based on lane position.
2.Propose maintenance options for identified defects,
considering optimal remedial measures and best practices.
Imagine a future where cars navigate our roads with the
intelligence of a seasoned driver. This project embarks on
that ambitious journey, unveiling a three-step roadmap
towards autonomous navigation. Our adventure begins
with Step A: Seeing the Road Clearly. Like putting on
glasses for the car, we calibrate its camera, correcting any
distortions and ensuring razor-sharp vision. Then, we
apply image processing wizardry, sharpening lane lines as
if tracing them on a map. Finally, we become meticulous
lane inspectors, measuring curvature and vehicle offset,
ensuring the car stays centered, even on winding roads.
With its vision honed, our smart car graduates to Step B:

Avoiding Troublemakers. Using the powerful "super
vision" of Haar cascades, it effortlessly identifies cars,
bikes, buses, and even pedestrians, weaving through
traffic like a seasoned pro. To further its wisdom, we delve
into the knowledge of experts, studying advanced driver-
assistance systems and obstacle detection techniques.
From this wellspring of insight, we understand the root
causes of potential problems and equip the car with the
foresight to avoid them. Finally, in Step C: Taking the
Wheel and Staying Healthy, we become the car's personal
driving instructor. Based on its lane position, we develop a
nuanced steering strategy, ensuring smooth maneuvering
and precise lane control. But our care goes beyond the
present. We analyze potential issues, like tire wear or
sensor drift, and recommend proactive maintenance,
keeping the car in top shape for the long haul. This three-
step odyssey is not just about technology; it's about
building trust. By mastering the art of seeing clearly,
avoiding danger, and maintaining health, we pave the way
for a future where self-driving cars become seamless
companions on our roads, navigating not just with
intelligence, but with the reassurance of a vigilant
guardian.
5.REMEDIAL MEASURES:
1.Camera Calibration Optimization:
Periodically recalibrate the camera system to ensure
accurate intrinsic and extrinsic parameters.
Implement an automated calibration check to detect any
deviations or changes in the camera setup.
2.Enhanced Lane Detection Techniques:
Investigate advanced lane detection algorithms to improve
the system's accuracy and robustness.
Explore deep learning approaches for lane detection,
considering their potential to handle complex road
scenarios.
3.Real-time Object Detection Enhancements:
Integrate state-of-the-art object detection models (e.g.,
YOLO, SSD) for more efficient and real-time obstacle
recognition.
Continuously update the object detection module to
include new object classes and improve detection
accuracy.
4.Dynamic Lane Maintenance Recommendations:
Implement a dynamic maintenance suggestion module
that adapts to varying road conditions.
Utilize road condition data and environmental factors to
tailor maintenance recommendations for specific
scenarios.
5.Adaptive Steering Control Strategies:
Investigate adaptive steering control strategies based on
real-time traffic and road conditions.
Explore machine learning algorithms for personalized
steering adjustments, considering individual driving
styles.
3. CONCLUSIONS
In conclusion, the developed Advanced Driver Assistance
System (ADAS) code demonstrates significant strides in
enhancing road safety through innovative computer vision
and control techniques. The system excels in reliable lane
detection, curvature calculation, obstacle recognition, and
steering control, contributing to the overall goal of
mitigating the risk of accidents. The integration of camera
calibration, real-time processing, and dynamic
maintenance suggestions underscores the code's
comprehensive approach to creating a robust ADAS. The
project successfully aligns with the imperative of fostering
safer driving experiences by empowering vehicles with
intelligent decision-making capabilities.
REFERENCES
[1] An Intelligent Driving Assistance System Based on
Lightweight Deep Learning Model: Published in year
2022
[2] “Automatic Lane Line Detection Using AI” - Tejaswini
TS, Nithyashree R, Vishrutha MR, Vanditha AB, Rahul
PM - International Research Journal of Modernization
in Engineering Technology and Science 2023
[3] “Machine Learning Techniques in ADAS: A Review” -
Abdallah Moujahid, Manolo Dulva Hina , Assia
Soukane , Andrea Ortalda
[4] “Lane Detection in Autonomous Vehicles: A Systematic
Review” Noor Jannah Zakaria 1 , Mohd Ibrahim
Shapiai, Rasli Abd Ghani1 , Mohd Najib Mohd Yassin,
Mohd Zamri Ibrahim Nurbaiti Wahid
BIOGRAPHIES
AAKASH A KUMAR
Student
School of Computing and
Information Technology
REVA University

ARPITH M
Student
REVA University
PRAJWAL U
Student
REVA University
VISMAY U R
Student
REVA University

A REVIEW ON MACHINE LEARNING IN ADAS

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