This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work properly. To choose correct combination of the available methods and to utilize the benefits of methods of different categories, an image processing-based technique as well as a method based on driver-vehicle interaction is used. In order to avoid driving distraction, any use of an intrusive method is prevented. A driving simulator is used to gather real data and then artificial neural networks are used in the structure of the designed system. Several tests were conducted on twelve volunteers while their sleeping situations during one day prior to the tests, were fully under control. Although the impact of the proposed system on the improvement of the detection accuracy is not remarkable, the results indicate the main advantages of the system are the reliability of the detections and robustness to the loss of the input signals. The high reliability of the drowsiness detection systems plays an important role to reduce drowsiness related road accidents and their associated costs.

Automated driving can no longer be referred to as hype or science fiction but rather a technology that has been gradually introduced to the market. The recent activities of regulatory bodies and the market penetration of automated driving systems (ADS) demonstrate that society is exhibiting increasing interest in this field and gradually accepting new methods of transport. Automated driving, however, does not depend solely on the advances of onboard sensor technology or artificial intelligence (AI). One of the essential factors in achieving trust and safety in automated driving is road infrastructure, which requires careful consideration. Historically, the development of road infrastructure has been guided by human perception, but today we are at a turning point at which this perspective is not sufficient. In this study, we review the limitations and advances made in the state of the art of automated driving technology with respect to road infrastructure in order to identify gaps that are essential for bridging the transition from human control to self-driving. The main findings of this study are grouped into the following five clusters, characterised according to challenges that must be faced in order to cope with future mobility: international harmonisation of traffic signs and road markings, revision of the maintenance of the road infrastructure, review of common design patterns, digitalisation of road networks, and interdisciplinarity. The main contribution of this study is the provision of a clear and concise overview of the interaction between road infrastructure and ADS as well as the support of international activities to define the requirements of road infrastructure for the successful deployment of ADS.

Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated.