The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.

Goal: This paper proposes a reliable stress and relaxation level estimation algorithm that is implemented in a portable, low-cost hardware device and executed in real time. The main objective of this work is to offer an affordable and “ready-to-go” solution for medical and personal environments, in which the detection of the arousal level of a person is crucial. Methods: To achieve meaningful identification of stress and relaxation, a fuzzy algorithm based on expert knowledge is built according to parameters extracted from physiological records. In addition to the heart rate, parameters extracted from the galvanic skin response and breath are employed to extend the results. Moreover, this algorithm achieves accurate results with a restricted computational load and can be implemented in a miniaturized low-cost prototype. The developed solution includes standard and actively shielded electrodes that are connected to an Arduino device for acquisition, while parameter extraction and fuzzy processing are conducted with a more powerful Raspberry Pi board. The proposed solution is validated using real physiological registers from 42 subjects collected using BIOPAC MP36 hardware. Additionally, a real-time acquisition, processing and remote cloud storage service is integrated via IoT wireless technology. Results: Robust identification of stress and relaxation is achieved, with F1 scores of 91.15% and 96.61%, respectively. Moreover, processing is performed using a 20-second sliding window; thus, only a small frame of context is required. Significance: This work presents a reliable solution for identifying stress and relaxation levels in real time, which can lead to the production of low-cost commercial devices for use in medical and personal environments.

Relaxation helps to reduce physical, mental and emotional pressure. Relaxation techniques generally enable a person to obtain calmness and well-being by reducing stress, anxiety or anger. When a person becomes calm the body reacts physiologically, producing the so-called Relaxation Response (RResp) which affects the organism in a positive manner, no matter if it is during a state of relaxation or in the middle of a stressful period. The goal of this work is to design a system capable of identifying automatically the RResps of a subject by analyzing a single physiological signal, the Galvanic Skin Response (GSR). To do so, a team composed of psychologists, neurologists and engineers designed two experiments for inducing RResps in the participants while their GSR signals were being collected. The team analyzed the data and identified three different levels of RResp that can be quantified using only two easily calculated GSR features. Moreover, the use of the surface produced by GSR and its linear approximation is totally novel. Finally, the data were classified using Decision Tree strategies for each of the experiments and, after seeing that the obtained trees were similar, the team synthesized them in a single classification system. The F1 values obtained by the generalized classifier scored between 0.966 and 1.000 for the data collected in both experiments.