scholarly journals Convolution Neural Network with Selective Multi-Stage Feature Fusion: Case Study on Vehicle Rear Detection

2018 ◽  
Vol 8 (12) ◽  
pp. 2468 ◽  
Author(s):  
Won-Jae Lee ◽  
Dong Kim ◽  
Tae-Koo Kang ◽  
Myo-Taeg Lim
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Vehicle Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Adaboost Algorithm ◽  
Multi Stage ◽  
Assistance Systems ◽  
Visualization Techniques

Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain characteristic vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithm using these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks.

scholarly journals Probabilistic Approach for Validation of Advanced Driver Assistance Systems

2005 ◽  
Vol 1910 (1) ◽  
pp. 20-28
Author(s):  
O. J. Gietelink ◽  
B. De Schutter ◽  
M. Verhaegen
Keyword(s):  
Randomized Algorithms ◽  
Methodological Approach ◽  
Probabilistic Approach ◽  
Performance Criteria ◽  
Cruise Control ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

This paper presents a methodological approach for validation of advanced driver assistance systems. The methodology relies on the use of randomized algorithms that are more efficient than conventional validation that uses simulations and field tests, especially with increasing complexity of the system. The methodology first specifies the perturbation space and performance criteria. Then, a minimum number of samples and a relevant sampling space are selected. Next, an iterative randomized simulation is executed; then the simulation model is validated with the use of hardware tests to increase the reliability of the estimated performance. The proof of concept is illustrated with some examples of a case study involving an adaptive cruise control system. The case study points out some characteristic properties of randomized algorithms with respect to the necessary sample complexity and sensitivity to model uncertainty. Solutions for these issues are proposed as are corresponding recommendations for research.

scholarly journals Real-Time Vehicle Detection Using Cross-Correlation and 2D-DWT for Feature Extraction

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Abdelmoghit Zaarane ◽  
Ibtissam Slimani ◽  
Abdellatif Hamdoun ◽  
Issam Atouf
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Cross Correlation ◽  
Vehicle Detection ◽  
Discrete Wavelet ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Step Potential ◽  
Assistance Systems

Nowadays, real-time vehicle detection is one of the biggest challenges in driver-assistance systems due to the complex environment and the diverse types of vehicles. Vehicle detection can be exploited to accomplish several tasks such as computing the distances to other vehicles, which can help the driver by warning to slow down the vehicle to avoid collisions. In this paper, we propose an efficient real-time vehicle detection method following two steps: hypothesis generation and hypothesis verification. In the first step, potential vehicles locations are detected based on template matching technique using cross-correlation which is one of the fast algorithms. In the second step, two-dimensional discrete wavelet transform (2D-DWT) is used to extract features from the hypotheses generated in the first step and then to classify them as vehicles and nonvehicles. The choice of the classifier is very important due to the pivotal role that plays in the quality of the final results. Therefore, SVMs and AdaBoost are two classifiers chosen to be used in this paper and their results are compared thereafter. The results of the experiments are compared with some existing system, and it showed that our proposed system has good performance in terms of robustness and accuracy and that our system can meet the requirements in real time.

scholarly journals The Driving Lesson as a Socio-Technical Situation. A Case Study on the Interaction between Learner Driver, Driving Instructor, and the Motor Car

2021 ◽  
Vol 17 (2) ◽  
pp. 6-21
Author(s):  
Alexander Schmidl
Keyword(s):  
Detailed Analysis ◽  
Human Body ◽  
Technical Information ◽  
Theoretical Background ◽  
The Body ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistance Systems

A micro-sociological examination of the driving lesson raises the following question: How is the interaction between learner driver and driving instructor structured in this technical setting, and what meaning can be ascribed in this threefold constellation to the vehicle with its various technical elements? This case study examines the orientation patterns which exist between the learner driver, the driving instructor, and the car, which together constitute a socio-technical triangle, and what actions the learner driver needs to learn to enable them to drive the car safely. The theoretical background to the study is provided by interactionist theories, which have been broadened to include a greater sensitivity for the body and technology, and a sociological reading of postphenomenology. Using a method based on this theoretical background and informed by workplace studies, this study observed and made audiovisual recordings of driving lessons. This approach made it possible to undertake a detailed analysis of the situations, reveal how the human body interacts with technology, and how a person’s attention responds to technical information. In these situations, the driving instructor takes on the role of the translator by mediating between various situational definitions—one’s own, that of the inexperienced learner driver, other motorists, and the driver assistance systems in the car. The driving instructor represents the driving school as an institution that is responsible for creating an intersubjectively arranged understanding of how to deal with technology and socio-technical situations.

scholarly journals Driver perceptions of advanced driver assistance systems: A case study

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1122
Author(s):  
Siti Fatimah Abdul Razak ◽  
Sumendra Yogarayan ◽  
Afizan Azman ◽  
Mohd Fikri Azli Abdullah ◽  
Anang Hudaya Muhamad Amin ◽  
...  
Keyword(s):  
Technology Use ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems ◽  
Convenience Sampling ◽  
Safety Applications

Background: Automobile manufacturers need to have an insight and understand how consumers, specifically drivers, respond to the advanced driver assistance systems (ADAS) technology in their manufactured vehicles. This study reveals drivers’ perceptions of Malaysia’s advanced driver assistance systems, which is currently lacking in the literature. So far, other studies have focused on countries that are unlike Malaysia’s multi-culture environment. Methods: A survey was designed and distributed using convenience sampling to obtain responses from licensed drivers. Questions included demographic and driving questions, the perceptions of benefits and obstacles relevant to ADAS use, vehicle decision-making, and technology use. Data were collected from 818 respondents who were licensed drivers in Malaysia. Results were then analysed using statistical approaches. Results: The findings indicated that 76.8% of drivers have a positive attitude towards ADAS technology, particularly safety applications when they are available. Regardless of the accuracy of these systems, acceptance of the technology may shift upon viewing or hearing messages of possible problems with ADAS. Conclusions: It can be concluded that the safety advantages of ADAS technology are less valued by drivers who do not have experience of road traffic accidents. Furthermore, acceptance of the technology could be undermined by assuming that the safety applications could be compromised.

scholarly journals Vision-based Vehicle Detection Survey

2016 ◽  
Vol 4 (1) ◽  
pp. 31
Author(s):  
Alex David S ◽  
K. Antony Kumar ◽  
S. Ravi Kumar
Keyword(s):  
Autonomous Vehicles ◽  
Vehicle Detection ◽  
Road Accident ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Night Time ◽  
Common Features ◽  
The Past ◽  
Feature Based ◽  
Assistance Systems

Nowadays thousands of drivers and passengers were losing their lives every year on road accident, due to deadly crashes between more than one vehicle. There are number of many research focuses were dedicated to the development of intellectual driver assistance systems and autonomous vehicles over the past decade, which reduces the danger by monitoring the on-road environment. In particular, researchers attracted towards the on-road detection of vehicles in recent years. Different parameters have been analyzed in this paper which includes camera placement and the various applications of monocular vehicle detection, common features and common classification methods, motion- based approaches and nighttime vehicle detection and monocular pose estimation. Previous works on the vehicle detection listed based on camera poisons, feature based detection and motion based detection works and night time detection.

Sign in / Sign up

Export Citation Format

Share Document