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.

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 Study of the influence of the exhaust line ultrasounds over the performance of the Blind Spot Warning System

2020 ◽  
Vol 10 (1) ◽  
pp. 91-104
Author(s):  
Cătălin Meiroşu
Keyword(s):  
Warning System ◽  
Blind Spot ◽  
Scanning System ◽  
Cruise Control ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driving Experience ◽  
Exhaust Line ◽  
Assistance Systems ◽  
Made In

AbstractDuring the previous years, the vehicle manufacturers have tried to equip their vehicles with as much technology as possible, making the driving experience for people easier than ever. Most of the modern vehicles come today with ADAS (Advanced Driver Assistance Systems) either for driving (E.g. Cruise Control, Blind Spot Warning) or Parking (E.g. Rear Ultrasonic Sensors, Rear View Camera). Since the vehicle come equipped with more technology, a major task in developing vehicle remains the integration of these ADAS system in the vehicle context with the other components. Since most of the components cope with each other on the vehicle level, some technologies are more affected by other components – such as the case of an ultrasound vehicle scanning system (Blind Spot Warning) and the Exhaust line that emits ultrasounds from the exhaust muffler. The aim of this paper is to study the influence of the exhaust line ultrasounds (ultrasounds that are emitted by the engine cycle and filtered in the exhaust line of the vehicle) over the detection performance of the Blind Spot Warning Ultrasound system. Since vehicles are sold with a wide variety of powertrains, the solution presented took into account also these differences between powertrains equipped. In order to test the solution, mock-ups of the vehicle were made in order to proof the robustness of the method.

scholarly journals Learning in AI Processor

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xinhua Wang ◽  
Weikang Wu
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistance Systems ◽  
On Chip ◽  
Performance Computing ◽  
Analyze Data

AI processor, which can run artificial intelligence algorithms, is a state-of-the-art accelerator,in essence, to perform special algorithm in various applications. In particular,these are four AI applications: VR/AR smartphone games, high-performance computing, Advanced Driver Assistance Systems and IoT. Deep learning using convolutional neural networks (CNNs) involves embedding intelligence into applications to perform tasks and has achieved unprecedented accuracy [1]. Usually, the powerful multi-core processors and the on-chip tensor processing accelerator unit are prominent hardware features of deep learning AI processor. After data is collected by sensors, tools such as image processing technique, voice recognition and autonomous drone navigation, are adopted to pre-process and analyze data. In recent years, plenty of technologies associating with deep learning Al processor including cognitive spectrum sensing, computer vision and semantic reasoning become a focus in current research.

Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shilpa Gite ◽  
Ketan Kotecha ◽  
Gheorghita Ghinea
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Real Time ◽  
Complete Analysis ◽  
Context Aware ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Content Type ◽  
Assistance Systems

Purpose This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques. Design/methodology/approach Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability. Findings There has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains. Research limitations/implications The research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers. Social implications As context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians. Originality/value This paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.

5G NETWORK STANDARDS AND TECHNOLOGIES AND THE POSSIBILITIES OF ITS IMPLEMENTATION

2020 ◽  
pp. 104-119
Author(s):  
O.G. Oksiyuk ◽  
R.S. Odarchenko ◽  
S.Yu. Dakov ◽  
Yu.А. Burmak ◽  
Т.V. Fediura
Keyword(s):  
Deep Learning ◽  
Augmented Reality ◽  
Internet Of Things ◽  
Radio Spectrum ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Fifth Generation ◽  
5G Network ◽  
Assistance Systems ◽  
Internet Of Things Technology

The paper investigates the architecture of the 5G network and proposed a variant of the SCOM based parameter monitoring system. You can use this system to collect and analyze network performance information, detect deviations, and notify them for timely removal. Technologies were introduced for the use of the fifth generation GPP mobile network of the fifth generation. Analyzed and proposed for implementation. The current capabilities of the 5th generation network and the technologies for their implementation were also explored. This paper analyzes and provides recommendations for the implementation of the following servers. 5G networks make it possible to significantly increase data rates through various radio access technologies (RATs), and through the use of new 5G NR (New Radio) radio spectrum. Smart Home and Smart Building are available in a variety of different Internet of Things (IoT) services: video surveillance, home automation and control, security management, and more. Virtual Reality (VR) service creates the illusion of moving a person to another world, affecting the sense organs, especially the sight (VR-glasses). Augmented Reality (Augmented Reality) Augmented Reality service combines a real environment with virtual objects. These services are intended not only for entertainment but also for science. The 5G network, along with IoT Internet of Things technology, with the help of Industrial IIoT (Industrial Internet of Things) sensors, as well as AI (Artificial Intelligence), can significantly increase the degree of automation of production. This gives the opportunity in real time to analyze large amounts of diverse data (Big Data), both on the basis of insights, and using machine and deep learning (Machine learning, Deep learning). These may include, for example, e-Health, Mission Critical Communication, Tactile Internet, and others. Unmanned transport may be used as part of the Smart City service, but may exist separately. Also on the 5G platform it is possible to implement ADAS (Advanced Driver-Assistance Systems) driver assistance systems.

scholarly journals Performance Guarantees for Hazard Based Lateral Vehicle Control

2002 ◽  
Author(s):  
Eric J. Rossetter ◽  
J. Christian Gerdes
Keyword(s):  
Control Force ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driving Experience ◽  
The Road ◽  
Performance Guarantees ◽  
System A ◽  
Assistance Systems ◽  
On The Road ◽  
Near Future

Today’s vehicles are incorporating many advanced driver assistance systems and in the near future it will be likely to have increased capabilities such as lanekeeping assist systems. These systems will be an integral part of the driving experience, aiding the driver in avoiding hazardous obstacles. One approach for these systems is to represent the hazards as artificial potential fields that add control inputs to move the vehicle towards safe regions on the road. This paper focuses on bounding the lateral motion of a vehicle for a lanekeeping system. A Lyapunov approach is used where the bounding function consists of the artificial potential energy associated with the controller, the kinetic energy in the lateral and yaw modes, and energy terms that are dependent on vehicle heading. In order to achieve this bound, a condition has to be met for the lookahead distance and the location of the control force (which can also be interpreted as a condition on the decoupling of lateral and yaw modes). Using this bound, a potential field gain can be chosen to guarantee collision avoidance with fixed lateral obstacles.

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