Name
Abstract | ||
|---|---|---|
Driving is a safety critical task that requires a high levels of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to monitor workload online using readily available and robust sensors accessible via the vehicle's Controller Area Network (CAN). In this paper, we present details of the Warwick-JLR Driver Monitoring Dataset (DMD) collected for this purpose, and to announce its publication for driver monitoring research. The collection protocol is briefly introduced, followed by statistical analysis of the dataset to describe its structure. Finally, the public release of the dataset, for use in both driver monitoring and data mining research, is announced. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2799250.2799286 | AutomotiveUI |
Field | DocType | Citations |
CAN bus,Data collection,Workload,Simulation,Real-time computing,Human–computer interaction,Mobile phone,Engineering,Cognition,Statistical analysis | Conference | 1 |
PageRank | References | Authors |
0.40 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Phillip Taylor | 1 | 8 | 5.60 |
| Nathan Griffiths | 2 | 388 | 34.25 |
| Abhir Bhalerao | 3 | 399 | 37.56 |
| Xu Zhou | 4 | 2 | 0.75 |
| Adam Gelencser | 5 | 2 | 0.75 |
| Thomas Popham | 6 | 6 | 3.89 |