AR Accident Simulation
Accident Simulator is an AR product designed at Phoenix On the Ground(POG) to prevent traffic accidents by simulating frequently happening accidents between cars and pedestrians. The application works based on deep learning data that perceive lanes.
I worked as an AR programmer and product designer to build a safe city together with a deep learning engineer and a project manager. We are continuously developing our content through user testing and researching the effectiveness of the app in decreasing future traffic accidents.
2019 – 2021
3D Character Design
The project successfully got funded by multiple public and private corporations and we are aiming to launch the app to the public.
A user scanning the QR code at the POG station to start the experience
For this project, I joined the team in the design and prototype phase. Business concepts and plans were built by the team leader.
The process I have not participated in is marked in grey.
Traffic accidents are increasing year by year, showing a decrease in kill figures and an increase in injured figures. It is important to figure out the focal reason for accidents and identify the different patterns. We found drivers’ and pedestrians’ failure to keep their eyes on the road is the main issue, which is less recognized.
The user-based insurance (UBI) market is increasing. Most of the companies reflect driver’s behavior on the bill, such as vehicle miles and acceleration/ decelerationHowever, those UBI strategies only focus on the cars, not focusing on how different actions drivers take.
“Planting memory of near-miss traffic accident”
Developing the ability to prepare for similar accidents by instilling memories of accidents is the most effective solution we found to the main cause of traffic accidents.
07. Design & develop
USER SCENARIO DESIGN
The traffic accident simulation happens at the site where accidents are prone to happen. The user uses one’s phone and goes through a vicarious experience.
Considering the context, we aimed to design a safe service, reminding users of the surrounding through visual and audio cues.
DEVELOP PHASE 1
DEEP LEARNING INTEGRATION
The service aims to provide a seamless user experience where the AR simulations are augmented at the right position, especially in vehicles.
I worked with a deep learning engineer who trained a deep learning model to differentiate between lanes and not-lanes and converted the data into coordination usable at Unity. I was responsible for using those data and augmenting the simulation at the right location on the screen.
Scene to train
Trained result showing lane in white and non-lane in black
08. Evaluate and result
Selected for the Scale-Up project from the Ministry of Land, Infrastructure and Transport, we got funded to conduct user testing to find out the effectiveness of the AR accident simulation.
A participant going through user testing
The survey and interview sought out to answer the following:
09. Further development
After we proved the effectiveness of augmented traffic accidents education, we expanded the scope of traffic accident simulation into three different types of perspectives.