Name
Abstract | ||
|---|---|---|
Detecting how a vehicle is steered and then alarming drivers in real time is of utmost importance to the vehicle and the driver's safety, since fatal accidents are often caused by dan- gerous steering. Existing solutions for detecting dangerous maneuvers are implemented either in only high-end vehicles or on smartphones as mobile applications. However, most of them rely on the use of cameras, the performance of which is seriously constrained by their high visibility requirement. Moreover, such an over/sole-reliance on the use of cameras can be a distraction to the driver. To alleviate these problems, we develop a vehicle steering detection middleware called V-Sense which can run on commodity smartphones without additional sensors or infrastructure support. Instead of using cameras, the core of V-Sense/ senses a vehicle's steering by only utilizing non-vision sensors on the smartphone. We design and evaluate algorithms for detecting and differentiating various vehicle maneuvers, including lane-changes, turns, and driving on curvy roads. Since V-Sense does not rely on use of cameras, its detection of vehicle steering is not affected by the (in)visibility of road objects or other vehicles. We first detail the design, implementation and evaluation of V-Sense and then demonstrate its practicality with two prevalent use cases: camera-free steering detection and fine-grained lane guidance. Our extensive evaluation results show that V-Sense is accurate in determining and differentiating various steering maneuvers, and is thus useful for a wide range of safety-assistance applications without additional sensors or infrastructure. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2742647.2742659 | MobiSys |
Field | DocType | Citations |
Distraction,Middleware,Visibility,Steering system,Use case,Computer science,Real-time computing,Embedded system | Conference | 26 |
PageRank | References | Authors |
1.10 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongyao Chen | 1 | 57 | 3.30 |
| Kyong-Tak Cho | 2 | 135 | 9.56 |
| Sihui Han | 3 | 40 | 1.82 |
| Zhizhuo Jin | 4 | 26 | 1.10 |
| Kang G. Shin | 5 | 14055 | 1487.46 |