Name
Abstract | ||
|---|---|---|
Recent studies reported a significant increase in the number of accidents caused by distracted walking. In this paper, we develop a mobile system that provides a timely warning to the pedestrian and the driver to reduce the chance of pedestrian-involved accidents. The proposed system performs pedestrian risk assessment to estimate the collision probability based on accurate user localization, user-phone-viewing event detection, and WiFi P2P-based vehicle to pedestrian communication. Depending on the resulting collision probability, the pedestrian (and the driver) is alerted to prevent accidents. The proposed system is implemented on a COTS smartphone, and experiments are conducted in a department parking lot. Experimental results demonstrate that it effectively calculates the collision probability and sends accordingly an alert message to the user in a timely manner leveraging its improved positioning accuracy, energy efficiency, and effective user context awareness. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computers and Society | Data mining,Pedestrian,Parking lot,Computer science,Efficient energy use,Collision probability,Risk assessment,Context awareness,Real-time computing |
DocType | Volume | Citations |
Journal | abs/1805.00442 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Myounggyu Won | 1 | 189 | 16.43 |
| Aawesh Shrestha | 2 | 0 | 0.34 |
| Yongsoon Eun | 3 | 77 | 23.26 |