Lane Change Detection Algorithm on Real World Driving for

Arbitrary Road Infrastructures Using Raspberry Pi

B. Dharani Bharath and G. Thirumalaiah

Department of ECE, Annamacharya University, New Boynapalli, Rajampet, Andhra Pradesh, India

Keywords: Raspberry Pi, MEMS Sensor, USB Webcam, Lane Change Detection Algorithm.

Abstract: The Lane Change Detection Algorithm on Real-World Driving for Arbitrary Road Infrastructures is a robust

system designed to enhance driver safety by monitoring and analyzing lane changes. Using a Raspberry Pi as

the core processor, the system integrates a USB web camera for real-time video capture and computer vision

algorithms to detect lane markings and vehicle positioning. A MEMS sensor monitors steering and lateral

movements, while ultrasonic sensors measure the proximity of nearby vehicles to ensure safe lane transitions.

Alerts for unsafe or improper lane changes are delivered audibly through a 3.5 mm jack speaker, and visual

feedback is displayed on an LCD. This system offers a comprehensive solution for real-time lane change

monitoring across diverse road environments, improving road safety and driver awareness.

1 INTRODUCTION

Lane changing is a critical maneuver in driving that

significantly impacts road safety, especially in

complex and arbitrary road infrastructures. The

ability to detect and analyze lane changes in real-time

can help mitigate accidents caused by abrupt or

unsafe transitions. This research presents a Lane

Change Detection Algorithm utilizing Raspberry Pi,

leveraging real-time video processing and sensor

integration to enhance driver awareness. The system

incorporates a USB camera for continuous lane

monitoring, MEMS sensors to capture vehicle

dynamics, and ultrasonic sensors to detect

surrounding obstacles. By analyzing vehicle

positioning and lateral movements, the algorithm

provides real-time feedback through an audio alert

system and an LCD display, ensuring safer driving

practices. This technology is designed to function

effectively across diverse driving conditions, making

it a valuable tool for intelligent transportation systems

and advanced driver assistance applications.

2 RELATED WORKS

Lane change detection is a critical aspect of

Advanced Driver Assistance Systems (ADAS) and

autonomous vehicle technologies. Various

approaches have been explored in the literature,

including traditional image processing techniques,

machine learning-based models, and sensor fusion

methods. This section reviews existing lane detection

and lane change detection techniques, highlighting

their advantages and limitations when applied to real-

world road infrastructures.

2.1 Lane Detection and Tracking

Techniques

Traditional Image Processing-Based Approaches:

Traditional lane detection relies on extracting lane

markings using image processing techniques such as:

• Edge Detection Methods: The Canny edge

detection algorithm has been widely used to

identify lane boundaries. For example, [He

and Wang, 2015] applied edge detection

followed by the Hough Transform to detect

straight lane markings. However, these

methods struggle with complex road

environments such as faded lane markings,

curved roads, and occlusions.

• Hough Transform-Based Methods: The

Hough Transform is effective in detecting

straight lane lines but often fails in curved

roads. To address this, [Kim et al., 2017] used

a modified probabilistic Hough Transform,

Bharath, B. D. and Thirumalaiah, G.

Lane Change Detection Algorithm on Real World Driving for Arbitrary Road Infrastructures Using Raspberry Pi.

DOI: 10.5220/0013883200004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

377-382

ISBN: 978-989-758-777-1

377

improving detection in challenging road

conditions.

• Perspective Transformation: Birds-eye-

view transformation techniques are used to

correct perspective distortions in lane images.

[Kumar et al., 2018] combined inverse

perspective mapping (IPM) with edge

detection for improved lane segmentation.

Machine Learning-Based Approaches: To

overcome the limitations of traditional methods,

machine learning-based approaches have been

explored:

• Support Vector Machines (SVM): [Chen et

al., 2019] used SVM classifiers trained on lane

pixel features for improved robustness in

varying lighting conditions.

• Convolutional Neural Networks (CNNs):

Deep learning methods such as CNNs have

shown superior accuracy in lane detection.

[Pan et al., 2020] introduced a CNN-based

model trained on large-scale datasets like

TuSimple and CULane, achieving state-of-

the-art results.

• Hybrid Approaches: Combining traditional

image processing with machine learning has

been proposed to enhance robustness. For

example, [Zhang et al., 2021] combined edge

detection with a CNN-based segmentation

network to achieve accurate lane

segmentation.

2.2 Lane Change Detection Techniques

Vision-Based Lane Change Detection: Vision-

based methods primarily use lane departure

information to detect lane changes.

• Optical Flow Techniques: Optical flow

methods track pixel movement in consecutive

frames to infer vehicle motion. [Rashid et al.,

2016] applied Lucas-Kanade optical flow to

detect lateral vehicle movement indicating a

lane change. However, these methods are

computationally expensive.

• Lane Marking Tracking: [Liu et al., 2018]

developed a lane tracking system that

continuously monitors lane markings and

detects deviations exceeding a threshold,

indicating a lane change.

• Deep Learning for Lane Change

Prediction: Recurrent Neural Networks

(RNNs) and Long Short-Term Memory

(LSTM) models have been used for predicting

lane changes based on past vehicle

trajectories. [Lee et al., 2019] proposed an

LSTM model trained on real-world driving

data to predict lane changes with high

accuracy.

Sensor Fusion-Based Lane Change Detection: In

addition to vision-based methods, sensor fusion

techniques integrate multiple data sources for

improved reliability.

• IMU and GPS-Based Approaches: [Wang et

al., 2020] fused inertial measurement unit

(IMU) and GPS data to detect lane changes

based on vehicle motion patterns.

• LiDAR and Radar Fusion: Some high-end

autonomous systems integrate LiDAR and

radar to detect lane changes. [Schwarz et al.,

2021] developed a LiDAR-based lateral

motion tracking system for lane change

detection. However, the high cost and power

requirements make these solutions impractical

for low-cost embedded systems like

Raspberry Pi.

2.3 Summary and Research Gaps

From the surveyed literature, the following research

gaps are identified:

• Lack of efficient low-power lane change

detection solutions suitable for embedded

platforms like Raspberry Pi.

• Limited real-world evaluation on

unstructured roads and arbitrary lane

configurations.

• Inadequate sensor fusion methods that

efficiently integrate IMU and vision-based

detection for accurate lane change prediction.

This paper proposes a hybrid lane change detection

algorithm using Raspberry Pi, integrating computer

vision, edge detection, and IMU-based motion

tracking to achieve real-time performance in diverse

driving conditions.

3 METHODOLOGY

3.1 Existing Methods

Traditional lane change detection systems primarily

rely on mechanical or single-sensor technologies such

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as side mirrors or basic ultrasonic sensors for

proximity detection. These systems are limited by

their inability to provide real-time video processing

or detect lane markings under diverse road

conditions. Additionally, they lack the capability to

integrate multiple data sources, such as vehicle

steering patterns or lateral movements, leading to

incomplete and less reliable results. These limitations

make traditional methods less effective in ensuring

safe lane transitions, especially on arbitrary road

infrastructures.

3.2 Proposed Method

The proposed system overcomes the limitations of

traditional methods by integrating multiple sensors

and advanced algorithms to provide a comprehensive

lane change detection solution. Utilizing Raspberry Pi

as the core processor, it combines a USB web camera

for real-time video capture, MEMS sensors to track

steering and lateral movements, and ultrasonic

sensors for proximity detection. The system processes

this data using computer vision and audio feedback

mechanisms to notify drivers of unsafe or improper

lane changes. The inclusion of an LCD for visual

feedback further enhances usability, making it an

efficient and reliable solution for diverse driving

conditions.

Figure 1: Block Diagram of the Proposed Method.

3.3 System Overview

The proposed lane change detection system consists

of:

• A Raspberry Pi 4 Model B for real-time video

processing

• A camera module (Raspberry Pi Camera v2)

for capturing road lane images

• An Inertial Measurement Unit (IMU) for

vehicle motion tracking

• A power supply (battery/power bank) and

peripheral connections

As shown in the figure 1 The system continuously

captures video frames, detects lanes, tracks vehicle

movement, and determines lane change events based

on vision and IMU data.

3.4 Data Acquisition

3.4.1 Video Input from Camera

o A forward-facing camera mounted on the

vehicle captures real-time road footage at

30 FPS.

o The camera’s field of view (FoV) is

adjusted to focus on the lane markings and

road structure.

3.4.2 IMU Data Collection

o An MPU6050 IMU sensor provides

accelerometer and gyroscope readings at

100 Hz sampling rate.

o The gyroscope measures yaw rate, and the

accelerometer detects lateral acceleration,

indicating vehicle movement.

3.5 Image Preprocessing

To enhance lane visibility and reduce noise, the

captured video frames undergo the following

processing steps:

3.5.1 Grayscale Conversion

o Converts the RGB image into a single-

channel grayscale image to simplify

processing.

3.5.2 Gaussian Blurring

o Applies a Gaussian filter to smooth the

image and reduce noise.

o Kernel size: 5×5 to maintain edge details

while reducing high-frequency noise.

Lane Change Detection Algorithm on Real World Driving for Arbitrary Road Infrastructures Using Raspberry Pi

379

3.5.3 Edge Detection using Canny

Algorithm

o Identifies lane boundaries by detecting

edges in the grayscale image.

o Lower and upper threshold values: 50

and 150, optimized for lane markings.

3.5.4 Region of Interest (ROI) Selection

o Crops the image to focus only on the road

lanes, removing unnecessary

background elements.

o A trapezoidal ROI is selected based on

empirical testing.

4 IMPLEMENTATION ON

RASPBERRY PI

• Programming Language: Python with

OpenCV for image processing, NumPy

for numerical computations, and SciPy

for sensor fusion.

• Hardware Optimization: Multi-

threading is implemented to parallelize

image processing and IMU data reading.

• Real-Time Processing: The system

achieves lane change detection within

200 ms latency, ensuring real-time

performance.

5 EXPERIMENTAL SETUP AND

VALIDATION

3.7.1 Test Environment

o The algorithm is tested on urban roads,

highways, and unstructured roads with

different lane conditions.

o Daytime, nighttime, and adverse weather

conditions (rain, fog, low light) are

considered.

3.7.2 Performance Metrics

o Lane detection accuracy (Percentage of

correctly detected lanes).

o Lane change detection precision and

recall (True Positive, False Positive

rates).

o Processing speed (Frame processing

time on Raspberry Pi). Table 1 Shows the

Summary of Proposed Methodology.

Table 1: Summary of Proposed Methodology.

Step

Technique Used

Purpose

Data

Acquisition

Camera + IMU

Capture road

and vehicle

motion data

Preprocessing

Grayscale,

Gaussian Blur,

Canny Edge

Detection

Enhance lane

visibility

Lane Detection

Hough

Transform +

Polynomial

Fitting

Identify lane

markings

Lane Tracking

Kalman Filter

Improve

robustness in

lane

estimation

Lane Change

Detection

Vision-based

lane position +

IMU sensor

fusion

Detect and

confirm lane

changes

Implementation

Python +

OpenCV on

Raspberry Pi

Real-time

processing

Validation

Real-world

testing +

Performance

metrics

Ensure

algorithm

accuracy

6 HARDWARE REQUIREMENTS

6.1 Raspberry Pi

To establish the lane detection ROI's distance from

the left side, we analyzed the picture and measured

the distance between white pixels (255). To achieve

this, the pixels are processed as vectors to establish

their positions McCall et al., (2006). Here, the

histogram comes into play. If the distance between

white pixels grows, the Arduino UNO is directed to

spin in the appropriate direction. When the distance

decreases, the Arduino UNO should turn to the left.

The needed degree of rotation is based on the

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observed distance. Before being evaluated in the

actual world, the generated model underwent

simulation testing.

The steering and speed models yield similar

anticipated values. The characteristics extracted from

the input image influence tracking and velocity

prediction. Speed increases on a straight route;

nevertheless, speed decreases while making a right or

left turn. Figure 2 Shows the Raspberry Pi board.

Figure 2: Raspberry Pi Board.

7 EXPERIMENTAL RESULT

The autonomous system uses three mechanisms: The

first mechanism employs a camera to detect

impediments. The second process involves the ability

to identify lanes and traffic signals, including stop

signs and lights. A GUI controller or self-operation

mode for systems. The computer assesses the frames

received from the Raspberry Pi using the chosen

model. After sending anticipated values to the

Raspberry Pi, the autonomous automobile travels

independently based on obstacle situations.The trials

primarily aim to identify different road types in

various environments. To do this, we conducted a

battery of experiments on several road photographs.

The autonomous system's needs were met through

configuration. Hardware was successfully

implemented. Both systems were launched after ACS

and RCS were configured as detailed in the

Implementation section. The implementation part

states that the autonomous mode was successful. A

camera module can recognize traffic signals,

including stop signs and lights, and follow a vehicle's

lane. Figures 3 and 4 showcase the hardware. Table 2

illustrates Comparative results.

Figure 3: Camera Setup for Object Recognition.

Figure 4: Raspberry Pi Configuration.

Table 2: Comparative Results.

Metric

Proposed

Method

Traditional

Hough [1]

Deep

Learning

[2]

Accuracy

(%)

92.3

84.1

95.7

FPR (%)

4.2

8.9

3.1

FPS

(Raspberry

Pi)

8 CONCLUSIONS

This study discusses a self-driving car system that can

navigate to a given place. In addition, the system

efficiently detects and avoids any obstructions in its

path. The suggested system has a general design and

may be placed on any vehicle, regardless of size. This

more accessible alternative to the current generation

of self-driving modules will pave the way for a bright

future for autonomous vehicles. An autonomous car

may drive itself based on information obtained from

Lane Change Detection Algorithm on Real World Driving for Arbitrary Road Infrastructures Using Raspberry Pi

381

sensors and machine learning algorithms. The system

was tested using a variety of situations designed to

simulate real-world conditions. After testing on

single-lane rails, we discovered that our strategy was

effective.

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