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 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

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 Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

2021 ◽  
Vol 11 (24) ◽  
pp. 11587
Author(s):  
Luca Ulrich ◽  
Francesca Nonis ◽  
Enrico Vezzetti ◽  
Sandro Moos ◽  
Giandomenico Caruso ◽  
...  
Keyword(s):  
Deep Learning ◽  
Expression Recognition ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driving Experience ◽  
Vehicle Accidents ◽  
Non Invasive ◽  
Driver Inattention ◽  
Assistance Systems ◽  
Invasive Techniques

Driver inattention is the primary cause of vehicle accidents; hence, manufacturers have introduced systems to support the driver and improve safety; nonetheless, advanced driver assistance systems (ADAS) must be properly designed not to become a potential source of distraction for the driver due to the provided feedback. In the present study, an experiment involving auditory and haptic ADAS has been conducted involving 11 participants, whose attention has been monitored during their driving experience. An RGB-D camera has been used to acquire the drivers’ face data. Subsequently, these images have been analyzed using a deep learning-based approach, i.e., a convolutional neural network (CNN) specifically trained to perform facial expression recognition (FER). Analyses to assess possible relationships between these results and both ADAS activations and event occurrences, i.e., accidents, have been carried out. A correlation between attention and accidents emerged, whilst facial expressions and ADAS activations resulted to be not correlated, thus no evidence that the designed ADAS are a possible source of distraction has been found. In addition to the experimental results, the proposed approach has proved to be an effective tool to monitor the driver through the usage of non-invasive techniques.

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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