Name
Abstract | ||
|---|---|---|
Stress is the cause of a large number of traffic accidents. The driver increases driving mistakes when he or she is in this mental state. Furthermore, the fuel consumption gets worse. In this paper, we propose an algorithm to estimate the optimum speed from the point of view of the stress level for each road section. When the driver completes a road section, the solution provides him or her with feedback. This feedback consists of recommendations such as: "You have driven too fast". The aim is that the driver adjusts speed when he or she repeats the trip. Optimization of the speed reduces stress and improves the driving from the point of view of energy saving. The optimal average speed is estimated using Particle Swarm Optimization (PSO) and MultiLayer Perceptron (MLP). The solution was deployed on Android mobile devices. The results show that the drivers drive smoother and reduce stress when they use the proposal. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICCE-Berlin.2015.7391220 | 2015 IEEE 5th International Conference on Consumer Electronics - Berlin (ICCE-Berlin) |
Keywords | Field | DocType |
Driving Assistant,Smart-Driving,Stress Driver,PSO,MLP | Particle swarm optimization,Android (operating system),Simulation,Computer science,Multilayer perceptron,Mobile device,Acceleration,Fuel efficiency,Mental state | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor Corcoba Magaña | 1 | 29 | 5.44 |
| Mario Munoz-Organero | 2 | 73 | 11.70 |