scholarly journals Towards Mindless Stress Regulation in Advanced Driver Assistance Systems: A Systematic Review

2020 ◽  
Vol 11 ◽  
Author(s):  
Adolphe J. Béquet ◽  
Antonio R. Hidalgo-Muñoz ◽  
Christophe Jallais
Keyword(s):  
Systematic Review ◽  
Stress Reduction ◽  
Driving Safety ◽  
Future Research ◽  
Response Modulation ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Stress Regulation ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

Background: Stress can frequently occur in the driving context. Its cognitive effects can be deleterious and lead to uncomfortable or risky situations. While stress detection in this context is well developed, regulation using dedicated advanced driver-assistance systems (ADAS) is still emergent.Objectives: This systematic review focuses on stress regulation strategies that can be qualified as “subtle” or “mindless”: the technology employed to perform regulation does not interfere with an ongoing task. The review goal is 2-fold: establishing the state of the art on such technological implementation in the driving context and identifying complementary technologies relying on subtle regulation that could be applied in driving.Methods: A systematic review was conducted using search operators previously identified through a concept analysis. The patents and scientific studies selected provide an overview of actual and potential mindless technology implementations. These are then analyzed from a scientific perspective. A classification of results was performed according to the different stages of emotion regulation proposed by the Gross model.Results: A total of 47 publications were retrieved, including 21 patents and 26 studies. Six of the studies investigated mindless stress regulation in the driving context. Patents implemented strategies mostly linked to attentional deployment, while studies tended to investigate response modulation strategies.Conclusions: This review allowed us to identify several ADAS relying on mindless computing technologies to reduce stress and better understand the underlying mechanisms allowing stress reduction. Further studies are necessary to better grasp the effect of mindless technologies on driving safety. However, we have established the feasibility of their implementation as ADAS and proposed directions for future research in this field.

scholarly journals Some Characteristics of Mental Models of Advanced Driver Assistance Systems: A Semi-structured Interviews Approach

2020 ◽  
Vol 64 (1) ◽  
pp. 1313-1317
Author(s):  
Michael A. Nees ◽  
Nithya Sharma ◽  
Karli Herwig
Keyword(s):  
Mental Models ◽  
Future Research ◽  
Negative Consequences ◽  
Cruise Control ◽  
Driver Assistance ◽  
Structured Interviews ◽  
Driver Assistance Systems ◽  
Cognitive Representations ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

People construct mental models—internal cognitive representations—when they interact with dynamic systems. The introduction of automation in vehicles has raised concerns about potential negative consequences of inaccurate mental models, yet characteristics of mental models remain difficult to identify. A descriptive study used semi-structured interviews to explore mental models of advanced driver assistance systems (adaptive cruise control, lane keeping assist, and Level 2 systems). Results exposed shortcomings in drivers’ understandings of the hardware, software, and limitations of these systems and also suggested that mental models will affect behavior while using automation. Further, we found that mental models can be influenced by interface feedback (or lack thereof) and limitations experienced. Some drivers attributed purposeful design to aspects of the systems that likely were chosen idiosyncratically or arbitrarily. Our findings offered potentially useful avenues for future research on mental models of automation and corroborated concerns that inaccurate mental models may be common.

Application of Advanced Driver-Assistance Systems in Police Vehicles

2021 ◽  
pp. 036119812110171
Author(s):  
Vanessa Nasr ◽  
David Wozniak ◽  
Farzaneh Shahini ◽  
Maryam Zahabi
Keyword(s):  
Police Officers ◽  
Motor Vehicle ◽  
Detection System ◽  
Driving Safety ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems ◽  
Police Vehicles ◽  
The Impact

Motor vehicle crashes are one of the leading causes of injuries and deaths for police officers. Advanced driver-assistance systems (ADAS) are driving control systems that have been found to improve civilian drivers’ safety; however, the impact of ADAS on police officers’ driving safety has yet to be investigated thoroughly. Disparities between driver states and tasks performed while driving between police and civilian drivers necessitate this distinction. This study identified the types of ADAS used in police vehicles, their impact on officers’ safety, and proposed potential future ADAS features to be implemented in police vehicles. A systematic literature review was conducted using Google Scholar, Compendex, Web of Science, Transport Research International Documentation (TRID), and Google Patents databases to identify the most prevalent police vehicles used in the U.S., available ADAS features in those vehicles, and the impact of ADAS on officers’ safety. A list of recommended ADAS features was developed based on the review of literature, authors’ knowledge and experience in the field, and the findings of an online survey with 73 police officers. Results indicated the addition of multiple ADAS features including the front vehicle detection system, intersection collision avoidance, evasive steering systems, left turn assist, traffic sign detection system, traffic jam assist, two lane and lane-ending detection, wrong-way alert, and autonomous highway driving features have the potential to improve officer safety and performance while driving. However, there was a void of studies focused on ADAS effects on police driving safety which needs to be addressed in future investigations.

Systematic Review of Research on Driver Distraction in the Context of Advanced Driver Assistance Systems

2021 ◽  
pp. 036119812110041
Author(s):  
Apoorva P. Hungund ◽  
Ganesh Pai ◽  
Anuj K. Pradhan
Keyword(s):  
Systematic Review ◽  
Task Performance ◽  
Secondary Task ◽  
Task Engagement ◽  
Driver Distraction ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Secondary Tasks ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

Advanced driver assistance systems (ADAS) promise improved driving performance and safety. With ADAS taking on more vehicle control tasks, the driver’s role may be reduced to that of passive supervision. This in turn may increase drivers’ engagement in non-driving-related tasks, thereby potentially reducing any promised safety benefit. We conducted a systematic review, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to study the relationship between ADAS use and driver distraction. Four research questions were addressed—two questions examined the effect of ADAS on secondary task engagement, and the quality of secondary task performance, and two addressed the effects of ADAS on driver attention and on driver behavior changes caused by secondary task engagement. Twenty-nine papers were selected for full text synthesis. The majority of the papers indicate an association between ADAS and increased secondary task engagement, as well as improved secondary task performance. Ten papers reported that drivers tend to divert their attention to secondary tasks and away from driving tasks. These outcomes highlight the continued importance of the role of the human driver despite vehicle automation, especially in the context of driver distraction, and that user understanding of ADAS functionalities and limitations is essential to appropriate and effective use of these systems.

scholarly journals Some Characteristics of Mental Models of Advanced Driver Assistance Systems: A Semi-structured Interviews Approach

2020 ◽  
Author(s):  
Michael Nees ◽  
Nithya Sharma ◽  
Karli Herwig
Keyword(s):  
Mental Models ◽  
Future Research ◽  
Negative Consequences ◽  
Cruise Control ◽  
Driver Assistance ◽  
Structured Interviews ◽  
Driver Assistance Systems ◽  
Cognitive Representations ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

People construct mental models—internal cognitive representations—when they interact with dynamic systems. The introduction of automation in vehicles has raised concerns about potential negative consequences of inaccurate mental models, yet characteristics of mental models remain difficult to identify. A descriptive study used semi-structured interviews to explore mental models of advanced driver assistance systems (adaptive cruise control, lane keeping assist, and Level 2 systems). Results exposed shortcomings in drivers’ understandings of the hardware, software, and limitations of these systems and also suggested that mental models will affect behavior while using automation. Further, we found that mental models can be influenced by interface feedback (or lack thereof) and limitations experienced. Some drivers attributed purposeful design to aspects of the systems that likely were chosen idiosyncratically or arbitrarily. Our findings offered potentially useful avenues for future research on mental models of automation and corroborated concerns that inaccurate mental models may be common.

Classification and Overview of Advanced Driver Assistance Systems According to the Driving Process

2010 ◽  
Author(s):  
Elizabeth Rendon-Velez
Keyword(s):  
Information Processing ◽  
Future Research ◽  
Critical Factors ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Automotive Safety ◽  
Advanced Driver Assistance Systems ◽  
Car Accidents ◽  
Assistance Systems ◽  
Basic Characteristics

In the field of automotive safety, Advanced Driver Assistance Systems (ADAS) that are systems designed to help the driver in its driver process, are receiving growing attention. Although the introduction of ADAS has contributed decreasing car accidents, the number of accidents is still high. This motivate us to explore the different available ADAS identifying the critical factors in order to find ways to enhance these systems. Some overviews and classifications of the ADAS field are available but the emphasis is more on the technical advances and the human machine interface rather than on the support they provide to the driver for the information processing. An overview and classification of the driver assistance systems field is presented with respect to that information processing performed by the driver when driving. The basic characteristics of these systems and the critical factors are provided. This approach allow for more appropriate identification of the priorities in the field of future research and development of ADAS regarding the driver. The proposed overview allocates the ADAS in 3 different categories on the basis of the sub process of driving they are helping (perception, analysis-decision, and action). This overview reveals that these systems besides being restricted by the sensors and the number of factors considered, they are specially limited in the consideration of drivers’ characteristics which is an important issue when human adaptation is considered.

Advanced driver assistance systems: Using multimodal redundant warnings to enhance road safety

2017 ◽  
Vol 58 ◽  
pp. 238-244 ◽  
Author(s):  
Francesco Biondi ◽  
David L. Strayer ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi ◽  
Claudio Mulatti
Keyword(s):  
Road Safety ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

MỘT PHƯƠNG PHÁP PHÁT HIỆN ĐIỂM MÙ VỚI ĐỘ TIN CẬY CAO VÀ THỜI GIAN THỰC CHO CÁC HỆ THỐNG HỖ TRỢ LÁI XE THÔNG MINH

2020 ◽  
pp. 47-58
Author(s):  
Sơn
Keyword(s):  
Region Of Interest ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

Các hệ thống hỗ trợ lái xe tiên tiến (Advanced Driver Assistance Systems: ADAS) đóng một vai trò quan trọng trong hệ thống an toàn chủ động chỉ có camera và các phương tiện tự động thông minh. Đối với các ứng dụng này, các yêu cầu về hiệu suất phát hiện đáng tin cậy và thời gian thực là các yếu tố cấp thiết. Bài báo này đề xuất giải pháp tối ưu tốc độ phát hiện ô tô và giảm các cảnh báo lỗi cho các hệ thống phát hiện điểm mù. Theo đó, trước tiên chúng tôi đề xuất bộ phân tầng Cascade – AdaBoost cùng với tập dữ liệu mẫu và thuật toán đào tạo của chúng tôi. Ngoài ra, để cải thiện tốc độ phát hiện, một kĩ thuật lựa chọn vùng quan tâm (Region of Interest: ROI) cũng được sử dụng để tránh trích xuất các vùng có khả năng tạo ra các cảnh báo lỗi như là bầu trời hoặc các vùng không phù hợp với phối cảnh. Phương pháp đề xuất đã tăng tốc độ phát hiện lên ít nhất 1,9 lần và giảm cảnh báo lỗi 2,24 lần so với phương pháp truyền thống ở các ảnh có độ phân giải cao (720 x 480) với tỷ lệ phát hiện đạt 99,4% và tỷ lệ cảnh báo lỗi nhỏ là 4,08%. Phương pháp đề xuất này có thể được ứng dụng cho các xe tự hành thông minh thời gian thực.

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