Xiping Hu
Abstract
Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and mood-fatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09% and 36.35%, respectively, compared to traditional smartphone-based music player under similar driving situations.
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Index Terms
- SAfeDJ: A Crowd-Cloud Codesign Approach to Situation-Aware Music Delivery for Drivers
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Reviews
RuayShiung Chang
Hu et al. describe a music recommendation system for drivers based on the driver's social condition and fatigue level and the road condition. It uses a smartphone for local calculation and the cloud for heavy recommendation computation. After listening to the music recommended by the system, the fatigue level and the degree of negative moods will be lower compared to the drivers who do not use the system. Thus, the road will hopefully be safer. The system is called SAfeDJ (short for "safe disc jockey"). First of all, the fatigue level is both an input and a measure of the system output, so there should be a continuous feedback system; the authors didn't consider this. Second, fatigue level and moods, though related, do not entirely equate to driving safety. The test of this paper is under a controlled environment. If put to a real road situation, the results may be very different due to the many variables in a real driving situation. It will be hard to tell which factors are more important in improving driving safety. If we have sensors to detect the fatigue level, why not keep warning and reminding the drivers to take a rest instead of using suitable music to improve road safety__?__ Playing music that the driver likes will not help much. In the system, the most difficult part is to install and connect all those sensors. Calculation is relatively easy after we have data, but this part is left out of the paper completely. Also, Figures 1 and 2 are the most important and should be explained in detail. Figure 3 is not necessary, and the personal data is obtained from the user profiles of Facebook, a very unreliable and inaccurate source. Finally, a driver's personal traits and habits are the most vital to driving safety. How to detect these bad behaviors is much more important than listening to suitable music. Online Computing Reviews Service
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