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 (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.

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

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 [...]

Imaging lidar prototype with homography and deep learning ranging methods

2022 ◽  
Author(s):  
Sehyeon Kim ◽  
Zhaowei Chen ◽  
Hossein Alisafaee
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Optical Element ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Lidar System ◽  
Time Performance ◽  
Potential Applications ◽  
Assistance Systems ◽  
Final System

Abstract We report on developing a non-scanning laser-based imaging lidar system based on a diffractive optical element with potential applications in advanced driver assistance systems, autonomous vehicles, drone navigation, and mobile devices. Our proposed lidar utilizes image processing, homography, and deep learning. Our emphasis in the design approach is on the compactness and cost of the final system for it to be deployable both as standalone and complementary to existing lidar sensors, enabling fusion sensing in the applications. This work describes the basic elements of the proposed lidar system and presents two potential ranging mechanisms, along with their experimental results demonstrating the real-time performance of our first prototype.

scholarly journals Fusión, clasificación y tracción simultáneas de obstáculos en movimiento mediante LIDAR y cámara usando algoritmo bayesiano

2020 ◽  
Vol 8 (1) ◽  
pp. 1-10
Author(s):  
Masrour Dowlatabadi ◽  
Ahmad Afshar ◽  
Ali Moarefianpour
Keyword(s):  
Real Data ◽  
Light Detection And Ranging ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Moving Obstacles ◽  
Assistance Systems ◽  
Near Future

In the near future, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The definite tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. It is believed that the perceived model of the situation can be improved by incorporating the obstacle classification. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle, pedestrian.

scholarly journals Rare Traffic Signs Recognition

2021 ◽  
Author(s):  
Tingir Badmaev ◽  
Vlad Shakhuro ◽  
Anton Konushin
Keyword(s):  
Control Systems ◽  
Autonomous Vehicles ◽  
Synthetic Data ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Traffic Signs ◽  
Road Signs ◽  
Automatic Mode ◽  
Assistance Systems ◽  
Rare Classes

Recognition of road signs is an important part of the control systems of autonomous vehicles and driver assistance systems. Modern recognition methods based on neural networks require large well-labeled datasets. Marking up data is quite expensive, but it is even more difficult to mark up rare classes of objects. To solve this problem in this article, we use synthetic data. We improve the marking of the Russian traffic signs dataset (RTSD) in semi-automatic mode adding 9 thousand new road signs. We perform an experimental evaluation of the currently best classifiers and detectors in the task of recognizing road signs. To improve the performance of classification, we use stochastic weight averaging (SWA) and contrastive loss. The use of modern methods allows us to train a high-quality neural network on synthetic data, which was previously impossible, and significantly improves the metrics of recognition of both rare and frequent road signs.

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