Automated driving: A literature review of the take over request in conditional automation

This article belongs to the Special Issue Autonomous Vehicles TechnologyIn conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver's state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions.This work was funded by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) Endowed Professorship for Sustainable Transport Logistics 4.0; the Spanish Ministry of Economy, Industry and Competitiveness under the TRA201563708-R and TRA2016-78886-C3-1-R project; open access funding by the Johannes Kepler University Linz

Cybernetics of the mind:learning individual's perceptions autonomously

In this article, we describe an approach to computational modeling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of heterogeneous raw data is proposed. At the lower level of the hierarchy, the raw data autonomously form semantically meaningful concepts. Instead of clustering based on visual or audio similarity, the concepts are formed at the second level of the hierarchy based on observed physiological variables (PVs) such as heart rate and skin conductance and are mapped to the emotional state of the individual. Wearable sensors were used in the experiments

Cybernetics of the mind:learning individual's perceptions autonomously

In this article, we describe an approach to computational modeling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of heterogeneous raw data is proposed. At the lower level of the hierarchy, the raw data autonomously form semantically meaningful concepts. Instead of clustering based on visual or audio similarity, the concepts are formed at the second level of the hierarchy based on observed physiological variables (PVs) such as heart rate and skin conductance and are mapped to the emotional state of the individual. Wearable sensors were used in the experiments

Stress Detection Using Frequency Spectrum Analysis of Wrist-Measured Electrodermal Activity

The article deals with the detection of stress using the electrodermal activity (EDA) signal measured at the wrist. We present an approach for feature extraction from EDA. The approach uses frequency spectrum analysis in multiple frequency bands. We evaluate the proposed approach using the 4 Hz EDA signal measured at the wrist in the publicly available Wearable Stress and Affect Detection (WESAD) dataset. Seven existing approaches to stress detection using EDA signals measured by wrist-worn sensors are analysed and the reported results are compared with ours. The proposed approach represents an improvement in accuracy over the other techniques studied. Moreover, we focus on time to detection (TTD) and show that our approach is able to outperform competing techniques, with fewer data points. The proposed feature extraction is computationally inexpensive, thus the presented approach is suitable for use in real-world wearable applications where both short response times and high detection performance are important. We report both binary (stress vs. no stress) as well as three-class (baseline/stress/amusement) results

Automated Driving: A Literature Review of the Take over Request in Conditional Automation

In conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver’s state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions