The number of vehicle accidents is rapidly increasing and causing significant economic losses in many countries. According to the World Health Organization, road accidents will become the fifth major cause of death by the year 2030. To minimize these accidents different types of collision warning systems have been proposed for motor vehicle drivers. These systems can early detect and warn the drivers about the potential danger, up to a certain accuracy. Many researchers study the effectiveness of these systems by using different methods, including Electroencephalography (EEG). From the literature review, it has been observed that, these systems increase the drivers' response and can help to minimize the accidents that may occur due to drivers unconsciousness. For these collision warning systems, tactile early warnings are found more effective as compared to the auditory and visual early warnings. This review also highlights the areas, where further research can be performed to fully analyze the collision warning system. For example, some contradictions are found among researchers, about these systems' performance for drivers within different age groups. Similarly, most of the EEG studies focus on the front collision warning systems and only give beep sound to alert the drivers. Therefore, EEG study can be performed for the rear end collision warning systems, against proper auditory warning messages which indicate the types of hazards. This EEG study will help to design more friendly collision warning system and may save many lives.
In many Asian countries, motorcyclists have a higher fatality rate as compared to other vehicles. Among many other factors, rear end collisions are also contributing for these fatalities. Collision detection systems can be useful to minimize these accidents. However, the designing of efficient and cost effective collision detection system for motorcyclist is still a major challenge. In this paper, an acoustic information based, cost effective and efficient collision detection system is proposed for motorcycle applications. The proposed technique uses the Short time Fourier Transform (STFT) to extract the features from the audio signal and Principal component analysis (PCA) has been used to reduce the feature vector length. The reduction of feature length, further increases the performance of this technique. The proposed technique has been tested on self recorded dataset and gives accuracy of 97.87%. We believe that this method can help to reduce a significant number of motorcycle accidents.
In many countries, the motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. To increase the safety of motorcyclists and minimize their road fatalities, this paper introduces a vision-based rear-end collision detection system. The binary road detection scheme contributes significantly to reduce the negative false detections and helps to achieve reliable results even though shadows and different lane markers are present on the road. The methodology is based on Harris corner detection and Hough transform. To validate this methodology, two types of dataset are used: (1) self-recorded datasets (obtained by placing a camera at the rear end of a motorcycle) and (2) online datasets (recorded by placing a camera at the front of a car). This method achieved 95.1% accuracy for the self-recorded dataset and gives reliable results for the rear-end vehicle detections under different road scenarios. This technique also performs better for the online car datasets. The proposed technique’s high detection accuracy using a monocular vision camera coupled with its low computational complexity makes it a suitable candidate for a motorbike rear-end collision detection system.