Driving safety has become a global topic of discussion with the recent development of the Smart Car concept. Many of the current car safety monitoring systems are based on image discrimination techniques, such as sensing the vehicle drifting from the main road, or changes in the driver's facial expressions. However, these techniques are either too simplistic or have a low success rate as image ...

Reducing the number of accidents that are caused by drowsiness or fatigue could be achieved by applying different strategies. This paper focuses on the on-line, real time detection of deteriorating driver state and driving behaviour, and on the question what should be done after this has been detected. It reports three studies performed in the TNO driving simulator. The first one leads to the c...

Automotive has gained several benefits from the Ambient Intelligent researches involving the deployment of sensors and hardware devices into an intelligent environment surrounding people, meeting users’ requirements and anticipating their needs. One of the main topics in automotive is to anticipate driver needs and safety, in terms of preventing critical and dangerous events. Considering the hi...

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver's attention becomes distracted, even for a short period of time. For the ...

Driving while drowsy is one of the significant causes road accidents, and detection drowsiness drivers necessary to minimize such accidents. There are many methods proposed for detection. In this paper, we present details a system using deep learning techniques with ECG as its input. Continuous wavelet transforms (CWT) applied on input signal, segmented 1-minute duration, then it converted into...