Detecting and Evaluating Risky Behaviors in Overtaking Scenarios for Safer Driving

dc.contributor.authorCheng, Junzhao
dc.contributor.authorLin, Yanchen
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mechanics and Maritime Sciencesen
dc.contributor.examinerDozza, Marco
dc.contributor.supervisorBackhouse, Andrew
dc.contributor.supervisorBjörnsson, Carina
dc.date.accessioned2025-07-02T09:47:09Z
dc.date.issued2025
dc.date.submitted
dc.description.abstractWhile cycling has become a popular way of modern transportation, concerns about cyclist safety remain significant. One typical and potentially risky scenario arises when motor vehicles pass (including overtake) cyclists, which may lead to close passing incidents and endanger the vulnerable road users. Previous works intensively investigated a typical scenario where an ego vehicle is overtaking a cyclist on a two-lane rural road with possible oncoming vehicles present on the adjacent lane. Experiments have been conducted to study the characteristics in vehicle-cyclist interaction during overtaking. Many studies segment the overtaking period into phases to better model the maneuver. There are also traffic laws that requires a minimum lateral distance when a motor vehicle passes cyclists. In this thesis, we used real-world data from Volvo Cars Corporation to detect and evaluate risky passing and overtaking behavior. We used and further developed a tool that extracts scenarios when ego vehicles pass cyclist. Then output information derived from signals in these scenarios. With that information, we built a rulebased model and applied it to different phases of the overtaking process. The model used four safety metrics: Variable-Adjusted Minimum Passing Distance, Perceived Risk Score, Minimum Distance Returning, and the Lateral-Time Risk Index. We conducted statistical analysis on the data and the model output. We also verified some of the results by manually reviewing the scenario on a visualization system to evaluate the correctness of our model. Furthermore, we discussed some special or difficult cases, such as overtaking a cyclist group, sensor unreliability at night, overtaking maneuvers happening on curved roads. We also pointed out the possible limitations in the data source, sensor fusion process and other factors. Lastly, we showed our perspective on the future works that can be based on our thesis work. The work presented in this thesis can help better understand how drivers overtake cyclists, and it may be useful for providing post-event feedback to enhance driving safety, or provide insights to user-based insurance systems.
dc.identifier.coursecodeMMSX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/309849
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectOvertaking
dc.subjectRisky Driving Detection
dc.subjectScenario Extraction
dc.subjectCyclist Safety
dc.subjectSafety Metrics
dc.subjectAdvanced Driver Assistance System (ADAS)
dc.subjectUser-Based Insurance
dc.subjectPost-Event Feedback
dc.titleDetecting and Evaluating Risky Behaviors in Overtaking Scenarios for Safer Driving
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc

Ladda ner

Original bundle

Visar 1 - 1 av 1
Hämtar...
Bild (thumbnail)
Namn:
2025 Junzhao Cheng & Yanchen Lin.pdf
Storlek:
1.69 MB
Format:
Adobe Portable Document Format

License bundle

Visar 1 - 1 av 1
Hämtar...
Bild (thumbnail)
Namn:
license.txt
Storlek:
2.35 KB
Format:
Item-specific license agreed upon to submission
Beskrivning: