scholarly journals ASSESSING THE DRIVER'S RISK PERCEPTION DURING AUTONOMOUS DRIVING

2020 ◽  
Vol 1 ◽  
pp. 1435-1444
Author(s):  
C. Gandrez ◽  
F. Mantelet ◽  
A. Aoussat ◽  
F. Jeremie ◽  
E. Landel
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
The Self ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
The Public ◽  
Automotive Market ◽  
Validation Plan ◽  
Assistance Systems ◽  
To Receive

AbstractAdvanced Driver-Assistance Systems were created to address the driver's failures. All these ADAS are a part of the evolution of the vehicles towards whole automation. To complete its launch in the automotive market, autonomous vehicles have to pass safety tests to acquire the consumers’ trust. To receive the approval of the public, the self-driving car has to take into account the human feeling. The risk perceived by the driver is one of the new emotional form to integrate at the validation plan. The purpose of this study is to examine the perception of the risk of a self-driving car's driver.

scholarly journals Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach

2021 ◽  
Vol 13 (8) ◽  
pp. 4264
Author(s):  
Matúš Šucha ◽  
Ralf Risser ◽  
Kristýna Honzíčková
Keyword(s):  
Autonomous Vehicles ◽  
Qualitative Data ◽  
Expert Interviews ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistant Systems ◽  
Vehicle Technology ◽  
Assistance Systems ◽  
Technical Solutions ◽  
Near Future

Globally, pedestrians represent 23% of all road deaths. Many solutions to protect pedestrians are proposed; in this paper, we focus on technical solutions of the ADAS–Advanced Driver Assistance Systems–type. Concerning the interaction between drivers and pedestrians, we want to have a closer look at two aspects: how to protect pedestrians with the help of vehicle technology, and how pedestrians–but also car drivers–perceive and accept such technology. The aim of the present study was to analyze and describe the experiences, needs, and preferences of pedestrians–and drivers–in connection with ADAS, or in other words, how ADAS should work in such a way that it would protect pedestrians and make walking more relaxed. Moreover, we interviewed experts in the field in order to check if, in the near future, the needs and preferences of pedestrians and drivers can be met by new generations of ADAS. A combination of different methods, specifically, an original questionnaire, on-the-spot interviewing, and expert interviews, was used to collect data. The qualitative data was analyzed using qualitative text analysis (clustering and categorization). The questionnaire for drivers was answered by a total of 70 respondents, while a total of 60 pedestrians agreed to complete questionnaires concerning pedestrian safety. Expert interviews (five interviews) were conducted by means of personal interviews, approximately one hour in duration. We conclude that systems to protect pedestrians–to avoid collisions of cars with pedestrians–are considered useful by all groups, though with somewhat different implications. With respect to the features of such systems, the considerations are very heterogeneous, and experimentation is needed in order to develop optimal systems, but a decisive argument put forward by some of the experts is that autonomous vehicles will have to be programmed extremely defensively. Given this argument, we conclude that we will need more discussion concerning typical interaction situations in order to find solutions that allow traffic to work both smoothly and safely.

Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles

2018 ◽  
Vol 7 (5) ◽  
pp. 18-25 ◽  
Author(s):  
Vipin Kumar Kukkala ◽  
Jordan Tunnell ◽  
Sudeep Pasricha ◽  
Thomas Bradley
Keyword(s):  
Autonomous Vehicles ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

A collection of easily deployable adversarial traffic sign stickers

2021 ◽  
Vol 69 (6) ◽  
pp. 511-523
Author(s):  
Henrietta Lengyel ◽  
Viktor Remeli ◽  
Zsolt Szalay
Keyword(s):  
Traffic Safety ◽  
Autonomous Driving ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Practical Possibility ◽  
Special Knowledge ◽  
Environment Perception ◽  
Assistance Systems ◽  
Traffic Sign Classification

Abstract The emergence of new autonomous driving systems and functions – in particular, systems that base their decisions on the output of machine learning subsystems responsible for environment perception – brings a significant change in the risks to the safety and security of transportation. These kinds of Advanced Driver Assistance Systems are vulnerable to new types of malicious attacks, and their properties are often not well understood. This paper demonstrates the theoretical and practical possibility of deliberate physical adversarial attacks against deep learning perception systems in general, with a focus on safety-critical driver assistance applications such as traffic sign classification in particular. Our newly developed traffic sign stickers are different from other similar methods insofar that they require no special knowledge or precision in their creation and deployment, thus they present a realistic and severe threat to traffic safety and security. In this paper we preemptively point out the dangers and easily exploitable weaknesses that current and future systems are bound to face.

Fuse -Replacement Noise-Filter Development for Automotive Audio–Video Navigation Systems Based on Loudness Isotropy Curves

2020 ◽  
Vol 25 (3) ◽  
pp. 83-92
Author(s):  
Bong-Seo Park ◽  
Hyun-cheol Park ◽  
Jung-jun Her
Keyword(s):  
Autonomous Driving ◽  
Navigation Systems ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Audio Quality ◽  
Noise Filter ◽  
Supply Noise ◽  
Low Pass ◽  
Assistance Systems ◽  
Audio Video

With the development of advanced driver assistance systems, the more reliable the autonomous driving technology is, the more the rest and entertainment times of the driver of the car increases. Hence, the importance of the entertainment function of automotive audio-video navigation (AVN) systems is increasing. Currently, the AVN system of automobiles has a monitoring function for fault diagnosis and a combination of functions. Applying these technologies is challenging for drivers who want to tune the audio quality to their musical taste. In this study, a method for upgrading the sound quality using a power supply noise filter without deforming the AVN system was developed. The low-pass attenuation that appeared as a side effect was solved by applying a filter using the loudness isotropic curve. In the installation method of the filter, the method of using a fuse holder minimized the inconvenience of AVN detachment and wiring. Based on the results obtained in this study, further research and improvement of the filter are required for audio tuning of various models.

scholarly journals Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 748 ◽  
Author(s):  
John E. Ball ◽  
Bo Tang
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Embedded Computing ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

Advanced driver assistance systems (ADAS) are rapidly being developed for autonomous vehicles [...]

scholarly journals Anticipatory experience in everyday autonomous driving

2020 ◽  
Vol 24 (6) ◽  
pp. 747-762
Author(s):  
Thomas Lindgren ◽  
Vaike Fors ◽  
Sarah Pink ◽  
Katalin Osz
Keyword(s):  
User Experience ◽  
Autonomous Driving ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Everyday Lives ◽  
Ethnographic Studies ◽  
Assistance Systems ◽  
Everyday Context

AbstractIn this paper, we discuss how people’s user experience (UX) of autonomous driving (AD) cars can be understood as a shifting anticipatory experience, as people experience degrees of AD through evolving advanced driver assistance systems (ADAS) in their everyday context. We draw on our ethnographic studies of five families, who had access to AD research cars with evolving ADAS features in their everyday lives for a duration of 1½ years. Our analysis shows that people gradually adopt AD cars, through a process that involves anticipating if they can trust them, what the ADAS features will do and what the longer-term technological possibilities will be. It also showed that this anticipatory UX occurs within specific socio-technical and environmental circumstances, which could not be captured easily in experimental settings. The implication is that studying anticipation offers us new insights into how people adopt AD in their everyday commute driving.

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