Using Technology Acceptance Model to Explain Driver Acceptance of Advanced Driver Assistance Systems

2019 ◽  
pp. 44-56
Author(s):  
Md Mahmudur Rahman ◽  
Shuchisnigdha Deb ◽  
Daniel Carruth ◽  
Lesley Strawderman
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems ◽  
Driver Acceptance ◽  
Acceptance Model

A Review of Empirical Studies on User Acceptance of Driver Assistance Systems

2015 ◽  
Vol 3 (3) ◽  
pp. 39-46
Author(s):  
Mohd Hafzi Md Isa ◽  
Baba Md Deros ◽  
Khairil Anwar Abu Kassim
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Policy Making ◽  
Empirical Studies ◽  
User Acceptance ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Behavioural Science ◽  
Assistance Systems ◽  
Acceptance Model

Objective - This paper presents a review of empirical research on technology acceptance with regards to driver assistance systems referred from 17 published studies. In addition to evaluating various behavioural science theories and models such as Technology Acceptance Model (TAM), other proposed variables that influence acceptance and usage of such technologies, potential limitations and gaps have also been analysed and evaluated. Methodology/Technique - Multiple electronic databases have been thoroughly searched for relevant empirical studies based on specified criteria and defined keywords. Findings - The results indicate that TAM indeed is a very useful model and mostly used; however, to better understand the user acceptance toward driver assistance systems, additional factors need to be included in order to complement the existing constructs in TAM. Novelty - it is essential to understand the concept of technology acceptance as it can assist in setting up priorities during the initial stages of product development and policy making Type of Paper - Conceptual Keywords: acceptance; driver assistance systems; car technology acceptance model; CTAM; technology acceptance model; TAM.

Effect of Driving Contexts on Driver Acceptance of Advanced Driver Assistance Systems

2017 ◽  
Vol 61 (1) ◽  
pp. 1944-1948 ◽  
Author(s):  
Md Mahmudur Rahman ◽  
Lesley Strawderman ◽  
Daniel W. Carruth
Keyword(s):  
Online Survey ◽  
Time Pressure ◽  
Time Of Day ◽  
Transportation Safety ◽  
Intention To Use ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems ◽  
Driver Acceptance

Advanced Driver Assistance Systems (ADASs) has been developed to enhance driver performance and comfort and improve transportation safety. The potential benefits of these technologies include: reduction in the number of crashes, enhanced vehicle control for drivers, reduced environmental impact, etc. However, for these technologies to achieve their potential, drivers must accept them and use them appropriately in traffic. This study investigated the effect of driving contexts on driver acceptance, more specifically, on the intention to use such technologies. Three contextual factors were considered: drivers’ fatigue level, time pressure, and time of day. Data collection was done using an online survey approach ( n = 386). Results found that fatigue and time pressure significantly affect drivers’ intention to use an ADAS. Results showed that drivers have increased intention to use an ADAS when they are fatigued or when there is no time pressure, as compared to a general driving condition.

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.

Gradient Reversal Domain Adaptation Pipeline in Advanced Driver Assistance Systems

2021 ◽  
Author(s):  
Francesco Rundo ◽  
Roberto Leotta ◽  
Sebastiano Battiato ◽  
Concetto Spampinato ◽  
Sabrina Conoci
Keyword(s):  
Domain Adaptation ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

Advanced Driver Assistance Systems (ADAS): Who’s Driving What and What’s Driving Use?

2021 ◽  
Vol 65 (1) ◽  
pp. 1220-1224
Author(s):  
Daniel Palac ◽  
Iiona D. Scully ◽  
Rachel K. Jonas ◽  
John L. Campbell ◽  
Douglas Young ◽  
...  
Keyword(s):  
Group Differences ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driver Safety ◽  
Advanced Driver Assistance Systems ◽  
Additional Group ◽  
Vehicle Technology ◽  
Assistance Systems ◽  
Vehicle Technologies ◽  
Safety Systems

The emergence of vehicle technologies that promote driver safety and convenience calls for investigation of the prevalence of driver assistance systems as well as of their use rates. A consumer driven understanding as to why certain vehicle technology is used remains largely unexplored. We examined drivers’ experience using 13 different advanced driver assistance systems (ADAS) and several reasons that may explain rates of use through a nationally-distributed survey. Our analysis focused on drivers’ levels of understanding and trust with their vehicle’s ADAS as well as drivers’ perceived ease, or difficulty, in using the systems. Respondents’ age and experience with Level 0 or Level 1 technologies revealed additional group differences, suggesting older drivers (55+), and those with only Level 0 systems as using ADAS more often. These data are interpreted using the Driver Behavior Questionnaire framework and offer a snapshot of the pervasiveness of certain driver safety systems.

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