Featured Application: Neural Network Structure Learning is expected to overcome difficulty of constructing a neural network structure.Abstract: This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly been designed on the basis of heuristic knowledge of an artificial intelligence (AI) expert. To overcome this limitation, in this study evolutionary approach (EA) has been utilized to automatically generate the proper artificial neural network (ANN) structures. VCGA has a new genetic operation called a chromosome attachment. By applying the VCGA, the initial ANN structures can be flexibly evolved toward the proper structure. The case study applied to the typical exclusive or (XOR) problem shows the feasibility of our methodology. Our approach is differentiated with others in that it uses a variable chromosome in the genetic algorithm. It makes a neural network structure vary naturally, both constructively and destructively. It has been shown that the XOR problem is successfully optimized using a VCGA with a chromosome attachment to learn the structure of neural networks. Research on the structure learning of more complex problems is the topic of our future research.
This paper describes the development of deep learning based human-centered threat assessment for application to automated driving vehicle. To achieve naturalistic driver model that would feel natural while safe to a human driver, manual driving characteristics are investigated through real-world driving test data. A probabilistic threat assessment with predicted collision time and collision probability is conducted to evaluate driving situations. On the basis of collision risk analysis, two kinds of deep learning have been implemented to reflect human driving characteristics for automated driving. A deep neural network (DNN) and recurrent neural network (RNN) are designed by neural architecture search (NAS), and by learning from the sequential data, respectively. The NAS is used to automatically design the individual driver’s neural network for efficient and effortless design process while ensuring training performance. Sequential trends in the host vehicle’s state can be incorporated through hand-made RNN. It has been shown from human-centered risk assessment simulations that two successfully designed deep learning driver models can provide conservative and progressive driving behavior similar to a manual human driver in both acceleration and deceleration situations by preventing collision.
This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using expensive and high-performance sensors, but limitations exist due to several practical issues. In respect of the application of autonomous driving cars in the near future, it is necessary to ensure autonomous driving performance by effectively utilizing sensors that are already installed for ADAS purposes. Additionally, the most common localization algorithm, which is usually used lane information only, has a highly unstable disadvantage in the absence of that information. Therefore, it is essential to ensure localization performance with other road features such as guardrails when there are no lane markings. In this study, we would like to propose a localization algorithm that could be implemented in the near future by using low-cost sensors and HD maps. The proposed localization algorithm consists of several sections: environment feature representation with low-cost sensors, digital map analysis and application, position correction based on map-matching, designated validation gates, and extended Kalman filter (EKF)-based localization filtering and fusion. Lane information is detected by monocular vision in front of the vehicle. A guardrail is perceived by radar by distinguishing low-speed object measurements and by accumulating several steps to extract wall features. These lane and guardrail information are able to correct the host vehicle position by using the iterative closest point (ICP) algorithm. The rigid transformation between the digital high definition map (HD map) and environment features is calculated through ICP matching. Each corrected vehicle position by map-matching is selected and merged based on EKF with double updating. The proposed algorithm was verified through simulation based on actual driving log data.
In this paper, we apply the neural architecture search (NAS) method to Korean grammaticality judgment tasks. Since the word order of a language is the final result of complex syntactic operations, a successful neural architecture search in linguistic data suggests that NAS can automate language model designing. Although NAS application to language has been suggested in the literature, we add a novel dataset that contains Korean-specific linguistic operations, which adds great complexity in the patterns. The result of the experiment suggests that NAS provides an architecture for the language. Interestingly, NAS has suggested an unprecedented structure that would not be designed manually. Research on the final topology of the architecture is the topic of our future research.
A genetic algorithm (GA) is a global search algorithm based on biological genetics. GAs are generally used for industrial applications, artificial neural networks, web applications, the defense industry, and so on. However, it is difficult to apply GAs to more complex situations because of the fixed number of chromosomes. In this research, in order to overcome this limitation, we propose a variable-chromosome GA with a chromosome attachment feature. Verification of the algorithm is carried out through anti-submarine high value unit (HVU) escort mission simulations. Ultimately, it is confirmed that the GA using the variable chromosome is more effective in dealing with highly complex missions, whereby the number of chromosomes gradually increases.
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