scholarly journals Machine Learning Based Framework for Vehicle Make and Model Recognition

2020 ◽  
Vol 9 (1) ◽  
pp. 1639-1643
Keyword(s):  
Machine Learning ◽  
Access Control ◽  
Control Systems ◽  
Traffic Monitoring ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Main Road ◽  
Assistance Systems ◽  
Toll Collection ◽  
Model Recognition

Nowadays, the speedy increase with in the rage of the automobiles on the main road and urban roads have created several challenges regarding the proper management and management of traffic. This is a very significant framework for intelligent traffic monitoring and management. Vehicle analysis is an important component for many smart applications, that includes automated toll collection, self-guided car driver assistance systems, smart car parking systems. In a recent years, due to increased security awareness in parking lots, restricted areas and building for access control systems, the need to identify and classify the vehicles has become important.

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 Robust pedestrian detection for driver assistance systems using machine learning

2020 ◽  
Vol 83 (2/3/4) ◽  
pp. 140
Author(s):  
Sabrine Hamdi ◽  
Souhir Sghaier ◽  
Hassene Faiedh ◽  
Chokri Souani
Keyword(s):  
Machine Learning ◽  
Pedestrian Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistance Systems

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.

A Unified Approach to Driver Assistance Systems Based on Artificial Potential Fields

1999 ◽  
Vol 123 (3) ◽  
pp. 431-438 ◽  
Author(s):  
J. Christian Gerdes ◽  
Eric J. Rossetter
Keyword(s):  
Control Systems ◽  
Potential Fields ◽  
Driver Assistance ◽  
Unified Approach ◽  
Driver Assistance Systems ◽  
Control Approach ◽  
Artificial Potential Fields ◽  
Individual Potential ◽  
Assistance Systems ◽  
Safe Set

This paper presents an approach to vehicle control based on the paradigm of artificial potential fields. Using this method, the dynamics of the vehicle are coupled with the environment in a manner that ensures that the system exhibits safe motion in the absence of driver inputs. The driver remains in control of the vehicle, however, with the control systems presenting a predictable and safe set of dynamics. With the control approach presented here, integration of various assistance systems is easily achieved through simple superposition of individual potential and damping functions. A simple example of a combined lanekeeping and stability system demonstrates how this can be accomplished. Preliminary simulation results suggest that both safety and driveability are achievable with such a system, prompting further investigation.

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.

scholarly journals Robust pedestrian detection for driver assistance systems using machine learning

2020 ◽  
Vol 83 (2/3/4) ◽  
pp. 140
Author(s):  
Chokri Souani ◽  
Souhir Sghaier ◽  
Hassene Faiedh ◽  
Sabrine Hamdi
Keyword(s):  
Machine Learning ◽  
Pedestrian Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistance Systems
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