Combining Surrogate Safety Measures and LiDAR technology: An Investigation.
(2024) In Master’s Theses in Mathematical Sciences FMAM05 20241Mathematics (Faculty of Engineering)
- Abstract
- Road safety is a critical concern for authorities worldwide. Traditional methods of monitoring traffic flow with today's technology usually involves visual observation or fixed sensor, which may have limitations in both accuracy and reliability. This study investigates the potential of LiDAR technology for traffic monitoring at intersections, with the aim of enhancing traffic safety.
The methodology involved recording LiDAR data which was later on preprocessed with background filtering, object clustering, object tracking and trajectory extraction. The goal was to identify vehicle movement and trajectories in a intersection, and for this the L-shape fitting algorithm and Savitzky-Golay filter were utilized. The research focuses on... (More) - Road safety is a critical concern for authorities worldwide. Traditional methods of monitoring traffic flow with today's technology usually involves visual observation or fixed sensor, which may have limitations in both accuracy and reliability. This study investigates the potential of LiDAR technology for traffic monitoring at intersections, with the aim of enhancing traffic safety.
The methodology involved recording LiDAR data which was later on preprocessed with background filtering, object clustering, object tracking and trajectory extraction. The goal was to identify vehicle movement and trajectories in a intersection, and for this the L-shape fitting algorithm and Savitzky-Golay filter were utilized. The research focuses on assessing the feasibility of using LiDAR to accurately capture and analyze traffic flow in intersections. With the use of surrogate safety measures the occurrence of possible traffic conflicts are then estimated and evaluated.
The findings from our study shows that the results from the surrogate measures does not correspond to the real events in the traffic. The source of error for this result is the high risk of false positives and the lack of material/data that was recorded. Although, previous studies on the use of LiDAR in traffic environments have shown that the technology does not face the same challenges as video-based systems do, but instead, it has shown to have great potential in gathering traffic data. We see a potential in more accurate results as a result of further improvement in the trajectory extraction. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9150346
- author
- Mokberi, Gloria LU and Haile, Lydia LU
- supervisor
-
- Karl Åström LU
- Carl Johnsson LU
- organization
- course
- FMAM05 20241
- year
- 2024
- type
- H2 - Master's Degree (Two Years)
- subject
- publication/series
- Master’s Theses in Mathematical Sciences
- report number
- LUTFMA-3529-2024
- ISSN
- 1404-6342
- other publication id
- 2024:E13
- language
- English
- id
- 9150346
- date added to LUP
- 2024-06-11 09:40:43
- date last changed
- 2024-06-11 09:40:43
@misc{9150346,
abstract = {{Road safety is a critical concern for authorities worldwide. Traditional methods of monitoring traffic flow with today's technology usually involves visual observation or fixed sensor, which may have limitations in both accuracy and reliability. This study investigates the potential of LiDAR technology for traffic monitoring at intersections, with the aim of enhancing traffic safety.
The methodology involved recording LiDAR data which was later on preprocessed with background filtering, object clustering, object tracking and trajectory extraction. The goal was to identify vehicle movement and trajectories in a intersection, and for this the L-shape fitting algorithm and Savitzky-Golay filter were utilized. The research focuses on assessing the feasibility of using LiDAR to accurately capture and analyze traffic flow in intersections. With the use of surrogate safety measures the occurrence of possible traffic conflicts are then estimated and evaluated.
The findings from our study shows that the results from the surrogate measures does not correspond to the real events in the traffic. The source of error for this result is the high risk of false positives and the lack of material/data that was recorded. Although, previous studies on the use of LiDAR in traffic environments have shown that the technology does not face the same challenges as video-based systems do, but instead, it has shown to have great potential in gathering traffic data. We see a potential in more accurate results as a result of further improvement in the trajectory extraction.}},
author = {{Mokberi, Gloria and Haile, Lydia}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master’s Theses in Mathematical Sciences}},
title = {{Combining Surrogate Safety Measures and LiDAR technology: An Investigation.}},
year = {{2024}},
}