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1. VISVESVARAYA TECHNOLOGICAL UNIVERSITY, BELAGAVI
(State University of Government of Karnataka)
Karnataka, India
“RAIL PLATFORM OBSTACLE DETECTION USING
LABVIEW SIMULATION”
Mini Project Synopsis
(5th
Sem, AY- 2025-26)
Presented by
Manasa.J.S : 1VW23UE031
&
Monika.S : 1VW23UE038
Under the Guidance of
Mr.Naveen.B
Assistant Professor
Department of Electronics and Communication Engineering
VISVESVARAYA TECHNOLOGICAL UNIVERSITY
VIAT, MUDDENAHALLI
Karnataka,India.

2. CONTENTS COVERED
CONTENTS COVERED
 Introduction
 Literature survey
 Problem statement
 Proposed methadology
 Expected outcomes
 References
 Plan of action week wise

3. INTRODUCTION :
• At present railways in one of the most widely used transportation
system in the world .
• Approximately billion freight tonne –kilometres are travelled around the
world every year and more the 5 billon passengers were travelled per
year as per Railway static report .
• But till now Railway transportation are not safe .
• Many countries Railway faces many collisions during in every year as a
result happened lot of damages and casualties.
• Railway safety is a major concern, especially at platform. Accidents occur
due to obstacles on tracks (e.g., luggage, animals, or people falling).Need
for real-time obstacle detection system.
• LabVIEW provides a graphical programming environment for hardware
integration and image/data processing
.

4. LITERATURE SURVEY
Ref Year Sensors Used Application Strengths Limitations
[1] Kim et al. 2008
Multiple 2D Laser
Scanners
UGV – drivable
environment detection
Reduces occlusion,
extends coverage,
reliable ground
estimation
Limited vertical
perception; requires
accurate alignment
[2] Maire 2007 Vision (mono/stereo)
Rail track
maintenance – anti-
collision
Low-cost, effective in
constrained rail
geometry
Sensitive to
lighting/weather;
possible false
detections
[3] Moon et al. 2007
Laser Scanner +
Vision
UGV – obstacle
detection
Sensor fusion
improves accuracy;
reduces false alarms
Needs precise
calibration &
synchronization
[4] Wender &
Dietmayer
2008
3D Laser Scanner +
Camera
Road vehicle detection
Accurate 3D
localization and
detection
High computational
cost; calibration
complexity
[5] Zhao et al. 2007
Multiple Lasers +
Cameras
Mobile platforms –
sensor calibration
Efficient extrinsic
calibration enables
reliable fusion
Requires re-
calibration; feature-
rich environments
needed
[6] Roberts & Corke 2000 2D Laser
Mining vehicles –
obstacle detection
Robust in dusty/harsh
environments
Cannot detect
overhangs; limited
FOV; dust
interference
[7] Oh et al. 2009 Stereo Vision
Railway platforms –
passenger safety
monitoring
Provides depth for
safety zone
monitoring; improves
passenger safety
Sensitive to
illumination; stereo
noise; real-time
challenges

5. PROBLEM STATEMENT
 Lack of automatic detection leads to delays &
accidents.
 Manual monitoring is inefficient.
 Requirement: Automated, reliable, real-time obstacle
detection.

6. PROPOSED METHODOLOGY
 The proposed system aims to detect obstacles on railway platforms
and tracks using a LabVIEW-based simulation environment.
 The methodology is divided into the following stages:
 System Analysis and Requirements
 Sensor Simulation in LabVIEW
 Data processing and decision Logic
 Alert and control mechanism
 Visualization and monitoring
 Testing and validation

7. EXPECTED OUTCOMES
 Successful Simulation in LabVIEW A working obstacle
detection system model will be developed and tested using
LabVIEW.
 Virtual Sensor Integration The simulation will accurately
represent the behavior of sensors (ultrasonic/IR or vision-
based) in detecting obstacles on railway platforms or tracks.

8. REFERENCES
 [1] J.H. Kim, S.H Lee and J.Ha. Kim, “Detection of a drivable environment
for UGV using multiple laser sensors,” International Conference on
Control, Automation and Systems, pp. 590-594, 2008.
 [2] F. Maire, “Vision based anti-collision system for rail track maintenance
vehicles,” Advanced Video and Signal Based Surveillance, IEEE, pp. 170-
175, 2007.
 [3] H.C. Moon, J.H. Kim and J.Ha. Kim, “Obstacle detecting system for
unmanned ground vehicle using laser scanner and vision,” International
Conference on Control, Automation and Systems, pp. 1758-1761, 2007.

9. PLAN OF ACTION
Week Tasks / Activities Date
Week 1
Literature survey on obstacle detection systems in railways. Study LabVIEW
basics and required toolkits.
30/08/2025
Week 2
Finalize problem statement, objectives, and scope. Collect reference papers
and existing models.
03/09/2025
Week 3
Learn sensor simulation methods in LabVIEW (Simulate Signal, DAQ
Assistant, Vision tools).
13/09/2025
Week 4
Design initial block diagram for the obstacle detection system. Define
thresholds and logic.
20/09/2025
Week 5 Implement basic sensor simulation (distance signal generation) in LabVIEW. 27/09/2025
Week 6 Develop decision-making logic using Comparison blocks and Case structures. 11/09/2025
Week 7
Integrate alarm/alert system (LEDs, buzzer sound simulation, control
outputs).
18/09/2025
Week 8
Create front panel UI for system monitoring (graphs, indicators, status
display).
25/09/2025
Week 9
Test system with different scenarios (no obstacle, single obstacle, multiple
obstacles).
01/09/2025
Week 10 Optimize design: remove false triggers, fine-tune thresholds, improve UI. 15/09/2025
Week 11
Documentation: Prepare results, screenshots of simulation, graphs, and
analysis.
17/09/2025
Week 12
Final project report preparation, presentation slides, and project
demonstration.
29/09/2025

10. /
Week Tasks / Activities
Week 1
Literature survey on
obstacle detection systems
in railways. Study
LabVIEW basics and
required toolkits.
Week 2
Finalize problem
statement, objectives, and
scope. Collect reference
papers and existing
models.
Week 3
Learn sensor simulation
methods in LabVIEW
(Simulate Signal, DAQ
Assistant, Vision tools).
Week 4
Design initial block
diagram for the obstacle
detection system. Define
thresholds and logic.
Week 5
Implement basic sensor
simulation (distance
signal generation) in
LabVIEW.
Week 6
Develop decision-making
logic using Comparison
blocks and Case
structures.
Week 7
Integrate alarm/alert
system (LEDs, buzzer
sound simulation, control
outputs).
Week 8
Create front panel UI for
system monitoring
(graphs, indicators, status
display).
Week 9
Test system with different
scenarios (no obstacle,
single obstacle, multiple
obstacles).
Week 10
Optimize design: remove
false triggers, fine-tune
thresholds, improve UI.
Week 11
Documentation: Prepare
results, screenshots of
simulation, graphs, and
analysis.
Week 12
Final project report
preparation, presentation
slides, and project
demonstration.