Intelligent Transportation Systems are advanced technologies used to reduce traffic
and increase road safety for vulnerable road users. Real-time traffic monitoring is an
important technology for collecting and reporting the information required to achieve
these goals through the detection and tracking of road users inside an intersection. To
be effective, these systems must be robust to all environmental conditions. This thesis
explores the fusion of camera and Light Detection and Ranging (LiDAR) sensors to
create an accurate and real-time traffic monitoring system. Sensor fusion leverages
complimentary characteristics of the sensors to increase system performance in low-
light and inclement weather conditions. To achieve this, three primary components
are developed: a 3D LiDAR detection pipeline, a camera detection pipeline, and a
decision-level sensor fusion module. The proposed pipeline is lightweight, running
at 46 Hz on modest computer hardware, and accurate, scoring 3% higher than the
camera-only pipeline based on the Higher Order Tracking Accuracy metric. The
camera-LiDAR fusion system is built on the ROS 2 framework, which provides a
well-defined and modular interface for developing and evaluated new detection and
tracking algorithms. Overall, the fusion of camera and LiDAR sensors will enable
future traffic monitoring systems to provide cities with real-time information critical
for increasing safety and convenience for all road-users. / Thesis / Master of Applied Science (MASc) / Accurate traffic monitoring systems are needed to improve the safety of road users.
These systems allow the intersection to “see” vehicles and pedestrians, providing near
instant information to assist future autonomous vehicles, and provide data to city
planers and officials to enable reductions in traffic, emissions, and travel times. This
thesis aims to design, build, and test a traffic monitoring system that uses a camera
and 3D laser-scanner to find and track road users in an intersection. By combining a
camera and 3D laser scanner, this system aims to perform better than either sensor
alone. Furthermore, this thesis will collect test data to prove it is accurate and able
to see vehicles and pedestrians during the day and night, and test if runs fast enough
for “live” use.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29905 |
| Date | January 2024 |
| Creators | Sochaniwsky, Adrian |
| Contributors | von Mohrenschildt, Martin, Habibi, Saeid, Computing and Software |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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