Abstract
In recent years, autonomous vehicles have become
increasingly popular, leading to extensive research on their safe
and efficient operation. Understanding road yielding behavior is
crucial for incorporating the appropriate driving behavior into
algorithms. This paper focuses on investigating drivers' yielding
behavior at unsignalized intersections. We quantified and
modelled the speed reduction time for vulnerable road users at
a zebra crossing using parametric survival analysis. We then
evaluated the impact of speed reduction time in two different
interaction scenarios, compared to the baseline condition of no
interaction through an accelerated failure time regression model
with the log-logistic distribution. The results demonstrate the
unique characteristics of each yielding behavior scenario,
emphasizing the need to account for these variations in the
modelling process of autonomous vehicles.
| Original language | English |
|---|---|
| Title of host publication | IEEE Proceedings Intelligent Transportation Systems Conference (ITSC) 2023 |
| Number of pages | 7 |
| Publication status | Published - Jul 2023 |
Fields of science
- 303 Health Sciences
- 303008 Ergonomics
- 201306 Traffic telematics
- 202031 Network engineering
- 202036 Sensor systems
- 202038 Telecommunications
- 202040 Transmission technology
- 203 Mechanical Engineering
- 211908 Energy research
- 211911 Sustainable technologies
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102002 Augmented reality
- 102003 Image processing
- 102013 Human-computer interaction
- 102015 Information systems
- 102019 Machine learning
- 102021 Pervasive computing
- 102024 Usability research
- 102026 Virtual reality
- 102029 Practical computer science
- 102034 Cyber-physical systems
- 501026 Psychology of perception
- 501 Psychology
- 501025 Traffic psychology
- 201305 Traffic engineering
- 202 Electrical Engineering, Electronics, Information Engineering
- 202003 Automation
- 202030 Communication engineering
- 202034 Control engineering
- 202035 Robotics
- 202037 Signal processing
- 202041 Computer engineering
- 203004 Automotive technology
- 211902 Assistive technologies
- 211909 Energy technology
- 211917 Technology assessment
- 501030 Cognitive science
JKU Focus areas
- Digital Transformation
- Sustainable Development: Responsible Technologies and Management
