Autonomous Vehicle Control for Lane and Vehicle Tracking by Using Deep Learning via Vision

2018 ◽  
Author(s):  
Masum Celil Olgun ◽  
Zakir Baytar ◽  
Kadir Metin Akpolat ◽  
Ozgur Koray Sahingoz
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Vehicle Tracking

scholarly journals A Survey of Deep Learning Applications to Autonomous Vehicle Control

2020 ◽  
pp. 1-22 ◽  
Author(s):  
Sampo Kuutti ◽  
Richard Bowden ◽  
Yaochu Jin ◽  
Phil Barber ◽  
Saber Fallah
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Vehicle Control

scholarly journals SP-ILC: Concurrent Single-Pixel Imaging, Object Location, and Classification by Deep Learning

Photonics ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 400
Author(s):  
Zhe Yang ◽  
Yu-Ming Bai ◽  
Li-Da Sun ◽  
Ke-Xin Huang ◽  
Jun Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Scheme ◽  
Autonomous Vehicle ◽  
Sampling Rate ◽  
Vehicle Control ◽  
Object Location ◽  
Concurrent Processes ◽  
Effective Use ◽  
High Degree ◽  
Single Pixel

We propose a concurrent single-pixel imaging, object location, and classification scheme based on deep learning (SP-ILC). We used multitask learning, developed a new loss function, and created a dataset suitable for this project. The dataset consists of scenes that contain different numbers of possibly overlapping objects of various sizes. The results we obtained show that SP-ILC runs concurrent processes to locate objects in a scene with a high degree of precision in order to produce high quality single-pixel images of the objects, and to accurately classify objects, all with a low sampling rate. SP-ILC has potential for effective use in remote sensing, medical diagnosis and treatment, security, and autonomous vehicle control.

Vision-based Autonomous Vehicle Recognition

2021 ◽  
Vol 54 (4) ◽  
pp. 1-37
Author(s):  
Azzedine Boukerche ◽  
Xiren Ma
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Comprehensive Evaluation ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Regional Security ◽  
Future Research ◽  
Learning Abilities ◽  
Vehicle Recognition ◽  
Model Recognition

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.

scholarly journals Automated Search-Based Robustness Testing for Autonomous Vehicle Software

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Kevin M. Betts ◽  
Mikel D. Petty
Keyword(s):  
Monte Carlo ◽  
Performance Metrics ◽  
Initial Conditions ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Test Case ◽  
Test Cases ◽  
Control Software ◽  
Robustness Testing ◽  
Testing Techniques

Autonomous systems must successfully operate in complex time-varying spatial environments even when dealing with system faults that may occur during a mission. Consequently, evaluating the robustness, or ability to operate correctly under unexpected conditions, of autonomous vehicle control software is an increasingly important issue in software testing. New methods to automatically generate test cases for robustness testing of autonomous vehicle control software in closed-loop simulation are needed. Search-based testing techniques were used to automatically generate test cases, consisting of initial conditions and fault sequences, intended to challenge the control software more than test cases generated using current methods. Two different search-based testing methods, genetic algorithms and surrogate-based optimization, were used to generate test cases for a simulated unmanned aerial vehicle attempting to fly through an entryway. The effectiveness of the search-based methods in generating challenging test cases was compared to both a truth reference (full combinatorial testing) and the method most commonly used today (Monte Carlo testing). The search-based testing techniques demonstrated better performance than Monte Carlo testing for both of the test case generation performance metrics: (1) finding the single most challenging test case and (2) finding the set of fifty test cases with the highest mean degree of challenge.

Autonomous Vehicle Control Design Framework for Performance and Driveability

2021 ◽  
Author(s):  
Ayhan Arda Araz ◽  
Erhan Ozkaya ◽  
Furkan Kocyigit ◽  
Enes Emre Bulut ◽  
Mertcan Cibooglu ◽  
...  
Keyword(s):  
Autonomous Vehicle ◽  
Control Design ◽  
Vehicle Control ◽  
Design Framework
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