Advanced driver-assistance systems (ADASs) have become a salient feature for safety in modern vehicles. They are also a key underlying technology in emerging autonomous vehicles. State-of-the-art ADASs are primarily vision based, but light detection and ranging (lidar), radio detection and ranging (radar), and other advanced-sensing technologies are also becoming popular. In this article, we present a survey of different hardware and software ADAS technologies and their capabilities and limitations. We discuss approaches used for vision-based recognition and sensor fusion in ADAS solutions. We also highlight challenges for the next generation of ADASs.

Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles

The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driver sleepiness detection (ADSD) systems. This study proposes an automatic sleepines ...

Automatic driver sleepiness detection using EEG, EOG and contextual information

The objective of this paper is to investigate if signal analysis and machine learning can be used to develop an accurate sleepiness warning system. The developed system was trained using the supposedly most reliable sleepiness indicators available, extracted from electroencephalography, electrocardiography, electrooculography, and driving performance data (steering behavior and lane positioning). Sequential forward floating selection was used to select the most descriptive features, and five different classifiers were tested. A unique data set with 86 drivers, obtained while driving on real roads in real traffic, both in alert and sleep deprived conditions, was used to train and test the classifiers. Subjective ratings using the Karolinska sleepiness scale (KSS) was used to split the data as sufficiently alert (KSS ≤ 6) or sleepy (KSS ≥ 8). KSS = 7 was excluded to get a clearer distinction between the groups. A random forest classifier was found to be the most robust classifier with an accuracy of 94.1% (sensitivity 86.5%, specificity 95.7%). The results further showed the importance of personalizing a sleepiness detection system. When testing the classifier on data from a person that it had not been trained on, the sensitivity dropped to 41.4%. One way to improve the sensitivity was to add a biomathematical model of sleepiness amongst the features, which increased the sensitivity to 66.2% for participant-independent classification. Future works include taking contextual features into account, using classifiers that takes full advantage of sequential data, and to develop models that adapt to individual drivers.

Driver Sleepiness Classification Based on Physiological Data and Driving Performance From Real Road Driving

The paper presents an approach and case study of a distributed driver monitoring system. The system utilizes smartphone sensors for detecting dangerous states for a driver in a vehicle. We use a mounted smartphone on a vehicle windshield directed towards the driver's face tracked by the front-facing camera. Using information from camera video frames as well as other sensors, we determine drowsiness, distraction, aggressive driving, and high pulse rate dangerous states that can lead to road accidents. We propose a cloud system architecture to capture statistics from vehicle drivers, analyze it and personalize the smartphone application for the driver. The cloud service provides reports on driver trips as well as statistics to developers. This allows to monitor and improve the system by developing modules for personification and taking into account context situation. We identified statistically that the driver eye closeness is related to the light brightness and drowsiness recognition should be adjusted accordingly.

Cloud-Based Driver Monitoring System Using a Smartphone

Face detection based on Adaboost and cascade classifier are studied in this paperHaar-Like features, computing method of integral image,detail definition of micro structural features and their description modes are discussedThe computing methods of four different kinds of rectangle features are also givenMoreover,color histogram is the feature of tracking,and face detection based on Adaboost which is combined effectively with tracking algorithm based on CamShift is proposedTo evaluate the performance of the proposed method,experiments have been conducted on video sequencesExperimental results show this proposed method can operate successfully detection and tracking in scale variation,view variation or occlusion

Face detection and tracking in video

Driving and transporting goods are necessary for human activity. As a consequence of drivers spending a considerable amount of time at the workplace, and usually under pressure, vehicular accidents have become a great contributor to mortality in several countries. Traffic accidents in countries such as the United States are a central concern. For instance, the U.S. National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System Encyclopedia [1] shows that there were approximately 55,926 vehicles involved in collisions in 2007, 9,797 of which were due to driver fatigue and inattention. The reported driver-related factors include the driver was drowsy, sleepy, asleep, and/or fatigued, the driver was under the influence of alcohol, drugs, and/or medication, the driver was inattentive (talking, eating, etc.), a cellular telephone was present in the vehicle, a cellular telephone was in use in vehicle.

Detecting Driver Drowsiness: A survey of system designs and technology.

Internet of things (IoT) devices are mostly ubiquitous in this day and age, and it is hard to imagine a life without them, especially in the productive sectors (industry, agriculture, and automotive) and in our daily life activities (consumer electronics, home automation, and intelligent buildings). The high demand for these devices has created significant competition to provide them at the best price, at the right time, and with the best features. The trend in which these devices have increased their product features has resulted in their embedded software being more complex, leading to extended development and testing times. Consequently, as the types of advanced IoT products keep diversifying, the field maintenance of all the different models deployed grows more complicated. This paper proposes an embedded software development framework for IoT devices independent of the microcontroller architecture, the compiler, and the development environment. This framework allows having a common software baseline between different projects, which shortens the learning curve, development time, and module validation while allowing code reuse for embedded software professionals. A proof-of-concept evaluation is also presented to demonstrate the efficiency and reliability of the obtained embedded software code for a simple but representative IoT application.

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Claudia Prieto-Resendiz

Papers

Detecting Driver Drowsiness: A survey of system designs and technology.

An Embedded Software Development Framework for Internet of Things Devices