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نتیجه جستجو - driver assistance

تعداد مقالات یافته شده: 5
ردیف عنوان نوع
1 Lane detector for driver assistance systems
آشکارساز خط برای سیستم های کمک راننده-2021
The challenging problem in the traffic system is lane detection. This Lane detection which attracts the computer vision community’s attention. For computer vision and machine learning techniques, the Lane detection which acts as the multi-feature detection problem. Many machine learning techniques are used for lane detection. Driver support system is one of the most important features in the modern vehicles to ensure the safety of the driver and decrease the vehicle accidents on road. Road Lane detection is the most challenging task and complex tasks now-a-days. Road localization and relative position between vehicle and roads which also includes with this. But in this journal, we propose a new method. Here, an on- board camera to be used which is looking outwards are presented here in this work. This proposed technique which can be used for all types of roads like painted, unpainted, curved, straight roads etc in different weather conditions. No need for cam- era calibration and coordination of the transform, may be any changing illumination situation, shadow effects, various road types. No representation for speed limits. This includes that the system acquires the front view using a camera mounted on the vehicles and detect the Lane by applying the code from the Python Programming process. This proposed system does not require any more information about roads. This system which demonstrates a robust performance for Lane detection.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Virtual Conference on Sustainable Materials (IVCSM-2k20).
Keywords: Lane detection | Computer vision | ITS | Driver support system | Machine learning techniques | Python programming | ADAS
مقاله انگلیسی
2 AITIA: Embedded AI Techniques for Embedded Industrial Applications
AITIA: تکنیک های هوش مصنوعی جاسازی شده برای کاربردهای صنعتی جاسازی شده-2020
New achievements in Artificial Intelligence (AI) and Machine Learning (ML) are reported almost daily by the big companies. While those achievements are accomplished by fast and massive data processing techniques, the potential of embedded machine learning, where intelligent algorithms run in resource-constrained devices rather than in the cloud, is still not understood well by the majority of the industrial players and Small and Medium Entereprises (SMEs). Nevertheless, the potential embedded machine learning for processing highperformance algorithms without relying on expensive cloud solutions is perceived as very high. This potential has led to a broad demand by industry and SMEs for a practical and applicationoriented feasibility study, which helps them to understand the potential benefits, but also the limitations of embedded AI. To address these needs, this paper presents the approach of the AITIA project, a consortium of four Universities which aims at developing and demonstrating best practices for embedded AI by means of four industrial case studies of high-relevance to the European industry and SMEs: sensors, security, automotive and industry 4.0.
Index Terms: artificial intelligence | machine learning | embedded hardware | sensors | network intrusion detection | driver assistance | industry 4.0
مقاله انگلیسی
3 Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment
تنظیم دقیق پارامترهای الگوریتم ADAS برای بهینه سازی ایمنی ترافیک و تحرک در محیط وسیله نقلیه همبند -2017
Under the Connected Vehicle environment where vehicles and road-side infrastructure can communicate wirelessly, the Advanced Driver Assistance Systems (ADAS) can be adopted as an actuator for achieving traffic safety and mobility optimization at highway facilities. In this regard, the traffic management centers need to identify the optimal ADAS algorithm parameter set that leads to the optimization of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. Once the ADAS-equipped drivers implement the optimal parameter set, they become active agents that work cooperatively to prevent traffic conflicts, and suppress the devel opment of traffic oscillations into heavy traffic jams. Measuring systematic effectiveness of this traffic management requires am analytic capability to capture the quantified impact of the ADAS on individual drivers’ behaviors and the aggregated traffic safety and mobility improvement due to such an impact. To this end, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through a multi-objective optimization approach that uses the Genetic Algorithm. The developed methodology is tested at a freeway facility under low, medium and high ADAS market penetration rate scenarios. The case study reveals that fine-tuning the ADAS algorithm parameter can significantly improve the throughput and reduce the traffic delay and conflicts at the study site in the medium and high penetration scenarios. In these scenarios, the ADAS algorithm parameter optimization is necessary. Otherwise the ADAS will intensify the behavior heterogeneity among drivers, resulting in little traffic safety improvement and negative mobility impact. In the high penetration rate scenario, the iden tified optimal ADAS algorithm parameter set can be used to support different control objectives (e.g., safety improvement has priority vs. mobility improvement has priority).
Keywords: Advanced Driver Assistance System (ADAS) | Driver behavior modeling | Microscopic traffic flow modeling | Traffic safety and mobility optimization
مقاله انگلیسی
4 Computer Vision in Automated Parking Systems: Design, Implementation and Challenges
دیدگاه کامپیوتر در سیستم های پارکینگ خودکار: طراحی، پیاده سازی و چالش ها-2017
Automated driving is an active area of research in both industry and academia. Automated Parking, which is automated driving in a restricted scenario of parking with low speed manoeuvring, is a key enabling product for fully autonomous driving systems. It is also an important milestone from the perspective of a higher end system built from the previous generation driver assistance systems comprising of collision warning, pedestrian detection, etc. In this paper, we discuss the design and implementation of an automated parking system from the perspective of computer vision algorithms. Designing a low-cost system with functional safety is challenging and leads to a large gap between the prototype and the end product, in order to handle all the corner cases. We demonstrate how camera systems are crucial for addressing a range of automated parking use cases and also, to add robustness to systems based on active distance measuring sensors, such as ultrasonics and radar. The key vision modules which realize the parking use cases are 3D reconstruction, parking slot marking recognition, freespace and vehicle/pedestrian detection. We detail the important parking use cases and demonstrate how to combine the vision modules to form a robust parking system. To the best of the authors knowledge, this is the first detailed discussion of a systemic view of a commercial automated parking system.
Keywords: Automated Parking | Automotive Vision | Autonomous Driving | ADAS | Machine Learning | Computer Vision | Embedded Vision | Safety critical systems
مقاله انگلیسی
5 Training My Car to See using Virtual Worlds
آموزش اتومبیل من برای دیدن با استفاده از جهان مجازی-2017
Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting in training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance.
Keywords: ADAS | Autonomous Driving | Computer Vision | Object Detection | Semantic Segmentation | Machine Learning | Data Annotation | Virtual Worlds | Domain Adaptation
مقاله انگلیسی
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