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Market readiness of on-board automotive software-intensive systems is tightly linked to the availability of appropriate certification schemes aimed at keeping the car makers confident and the consumers safe - especially in the context of Autonomous Driving, which is the next frontier of the automotive industry. Advanced driver assistance systems (ADAS) are going to be pervasively used in modern automobiles...
Intelligent automobiles and advanced driver assistance systems (ADAS) are some of the major technological developments that affect human daily life. Today, many studies are being generated to develop state of the art transportation systems. The general objective in these studies is to cope with negative effects of traffic. In this work, our aim is to contribute to the development of ADAS by determining...
Due to the advancement of automobile technology and increasing consumers demands, it is expected that automatic driving vehicles and manual driving vehicles will coexist in future automobile society. There are a number of people who are interested in driving and, they may think that the automatic driving vehicles are unnecessary. However, if the vehicle is operated manually, there is a possibility...
Vehicle taillights detection is an important topic in collision avoidance and in the field of autonomous vehicles. Analyzing the behavior of the front vehicle can prevent possible accidents. In this paper, a method for detecting vehicle taillights is presented. First, the system detects vehicles and then searches for candidate taillight pairs inside the obtained vehicles. Two methods for detecting...
An autopilot system includes several modules, and the software architecture has a variety of programs. As we all know, it is necessary that there exists one brand with a compatible sensor system till now, owing to complexity and variety of sensors before. In this paper, we apply (Robot Operating System) ROS-based distributed architecture. Deep learning methods also adopted by perception modules. Experimental...
In industrial process, some important variables such as quality index, efficiency index and concentration of product components are difficult or even impossible to be measured directly due to the limitation of technology. This phenomenon leads to few labeled data and plenty of unlabeled data. Traditional identification method for controlled auto regressive (CAR) model usually cannot deal with unlabeled...
We introduce the Concurrent Activity Recognizer (CAR) --- an efficient deep learning structure that recognizes complex concurrent teamwork activities from multimodal data. We implemented the system in a challenging medical setting, where it recognizes 35 different activities using Kinect depth video and data from passive RFID tags on 25 types of medical objects. Our preliminary results showed our...
Autonomous driving has been a hot topic with companies like Google, Uber, and Tesla because of the complexity of the problem, seemingly endless applications, and capital gain. The technology's brain child is DARPA's autonomous urban challenge from over a decade ago. Few companies have had some success in applying algorithms to commercial cars. These algorithms range from classical control approaches...
Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated training data. This time consuming process has begun impeding the progress of these deep learning efforts. This paper describes a method to incorporate photo-realistic computer images from...
Transportation systems might be heavily affected by factors such as accidents and weather. Specifically, inclement weather conditions may have a drastic impact on travel time and traffic flow. This study has two objectives: first, to investigate a correlation between weather parameters and traffic flow and, second, to improve traffic flow prediction by proposing a novel holistic architecture. It incorporates...
Extracting and identifying objects in very high resolution imagery has been a popular research topic in remote sensing. Since the beginning of this decade, deep learning techniques have revolutionized computer vision providing significant performance gains compared to traditional “shallow” techniques in various challenging vision problems. The training of deep neural networks usually requires very...
This paper presents an approach for real-time car parking occupancy detection that uses a Convolutional Neural Network (CNN) classifier running on-board of a smart camera with limited resources. Experiments show that our technique is very effective and robust to light condition changes, presence of shadows, and partial occlusions. The detection is reliable, even when tests are performed using images...
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