Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

A Survey on Multi-Objective Neural Architecture Search

Recently, the expert-crafted neural architectures is increasing overtaken by the utilization of neural architecture search (NAS) and automatic generation (and tuning) of network structures which has a close relation to the Hyperparameter Optimization and Auto Machine Learning (AutoML). After the earlier NAS attempts to optimize only the prediction accuracy, Multi-Objective Neural architecture Search (MONAS) has been attracting attentions which considers more goals such as computational complexity, power consumption, and size of the network for optimization, reaching a trade-off between the accuracy and other features like the computational cost. In this paper, we present an overview of principal and state-of-the-art works in the field of MONAS. Starting from a well-categorized taxonomy and formulation for the NAS, we address and correct some miscategorizations in previous surveys of the NAS field. We also provide a list of all known objectives used and add a number of new ones and elaborate their specifications. We have provides analyses about the most important objectives and shown that the stochastic properties of some the them should be differed from deterministic ones in the multi-objective optimization procedure of NAS. We finalize this paper with a number of future directions and topics in the field of MONAS.Comment: 22 pages, 10 figures, 9 table

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Multi-Objective Simulated Annealing for Hyper-Parameter Optimization in Convolutional Neural Networks

In this study, we model a CNN hyper-parameter optimization problem as a bi-criteria optimization problem, where the first objective being the classification accuracy and the second objective being the computational complexity which is measured in terms of the number of floating point operations. For this bi-criteria optimization problem, we develop a Multi-Objective Simulated Annealing (MOSA) algorithm for obtaining high-quality solutions in terms of both objectives. CIFAR-10 is selected as the benchmark dataset, and the MOSA trade-off fronts obtained for this dataset are compared to the fronts generated by a single-objective Simulated Annealing (SA) algorithm with respect to several front evaluation metrics such as generational distance, spacing and spread. The comparison results suggest that the MOSA algorithm is able to search the objective space more effectively than the SA method. For each of these methods, some front solutions are selected for longer training in order to see their actual performance on the original test set. Again, the results state that the MOSA performs better than the SA under multi-objective setting. The performance of the MOSA configurations are also compared to other search generated and human designed state-of-the-art architectures. It is shown that the network configurations generated by the MOSA are not dominated by those architectures, and the proposed method can be of great use when the computational complexity is as important as the test accuracy

Radar based on automotive pedestrian detection using the micro Doppler effects

Orientador: Prof. Dr. Alessandro ZimmerDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 29/08/2018Inclui referências: p.74-78Resumo: O desenvolvimento do carro autônomo é hoje em dia uma prática comum entre as maiores indústrias automotivas, e também em indústrias tecnológicas, como o Google e a Apple. Ao adicionar mais sensores, o veículo é capaz de se movimentar sozinho, identificar a trajetória correta, a distância para outros carros, e também a presença de objetos e seres vivos. Entretanto, existem muitos aspectos bloqueando o lançamento do carro autônomo. Como exemplo aspectos técnicos, como o caso do reconhecimento de pedestres. Embora, esse tópico seja abundantemente estudado para o uso de câmeras digitais, as mesmas não possuem confiabilidade nas medições de velocidade e distância, e ainda apresentam péssimos resultados quando há variação ou a falta de luz no ambiente. Baseado no que foi mencionado anteriormente, o foco dessa dissertação é de desenvolver e discutir a eficiência de um sistema de rápida identificação de pedestres, utilizando um novo radar de 79GHz de frequência. O principal objetivo é reconhecer o pedestre o mais rápido possível utilizando os efeitos micro Doppler do movimento humano em situações muito próximas de um acidente, junto com o método de classificação support vector machine (SVM). Objetivando essa meta algumas técnicas são usadas ao longo do trabalho. Primeiramente, a resolução de velocidade é melhorada com técnicas de otimização multiobjetivos, como algoritmos genéticos e random search para extrair o micro efeito Doppler. Então as informações de velocidade e distância são medidas pelo radar. Em sequência, um método de extração de características chamado de video temporal gradiente é aplicado. O método de machine learning SVM classifica os objetos em pedestre e não pedestres, com quadro diferentes métodos de treinamento. Por fim, é possível ver as vantagens do método de otimização que consegue atingir uma resolução de velocidade de 0,12 m/s. A comparação dos modelos de SVM mostra que o quarto modelo, utilizando kernel polinomial, apresenta os melhores resultados com uma acurácia de 99,5%. Entretanto, o tempo de processamento não é bom o suficiente, levando 72 ms para a classificação de um objeto. Palavras-Chaves: Carro autônomo. Reconhecimento de pedestres. Micro Doppler. Otimização multiobjectivos. Support vector machine.Abstract: The development of the autonomous car is nowadays a common practice in all the greatest automotive factories in the world, also in companies outside the automotive market, like Google and Apple. By adding more sensors, the vehicle is now capable of moving alone, identifying the correct path, the distance from another cars, also the presence of objects and people. However, there are still many issues blocking the autonomous car to be released. There are technical aspects to be solved, as the pedestrian recognition issues. Although, the recognition is widely studied and applied using cameras and digital images, there are issues to be improved. Like the distance and velocity reliability and the problems occurred because the lack of light in the environment. Based on the before mentioned, the focus in this presented work is to develop and discuss the efficiency of a pedestrian recognition system, using one automotive radar of 79 GHz. The main goal is to early detect the pedestrian using the micro Doppler characteristics of a human body in near to crash situations. Aiming this goal some techniques are used in the work. Firstly, the velocity resolution is improved, in order to extract the micro Doppler characteristics of the objects. The improvement of velocity resolution is reached by the use of multiobjective optimization techniques, as genetic algorithm and random search. The information about velocity and range is measured by the radar. In sequence a simple feature extraction method called video temporal gradient transform the data. The result is used in a machine learning technique called support vector machine (SVM). Which classifies the objects between pedestrians and non-pedestrians, with four different approaches. Concluding the work, it is possible to see the advantages of the multiobjective optimization in order to extract the micro Doppler effects. The optimization reached the velocity resolution of 0,12 m/s. The SVM comparison show that the fourth model with a polynomial kernel presented better result with accuracy 99,5%. However, the processing time of the system was not good enough taking 72 ms to identify an object. Keywords: Autonomous car. Pedestrian recognition. Micro Doppler. Multiobjective optimization. Support vector machine

Efficient Learning Machines

Computer scienc

A Deep-Learning Framework to Predict the Dynamics of a Human-Driven Vehicle Based on the Road Geometry

Many trajectory forecasting methods, implementing deterministic and stochastic models, have been presented in the last decade for automotive applications. In this work, a deep-learning framework is proposed to model and predict the evolution of the coupled driver-vehicle system dynamics. Particularly, we aim to describe how the road geometry affects the actions performed by the driver. Differently from other works, the problem is formulated in such a way that the user may specify the features of interest. Nonetheless, we propose a set of features that is commonly used for automotive control applications to practically show the functioning of the algorithm. To solve the prediction problem, a deep recurrent neural network based on Long Short-Term Memory autoencoders is designed. It fuses the information on the road geometry and the past driver-vehicle system dynamics to produce context-aware predictions. Also, the complexity of the neural network is constrained to favour its use in online control tasks. The efficacy of the proposed approach was verified in a case study centered on motion cueing algorithms, using a dataset collected during test sessions of a non-professional driver on a dynamic driving simulator. A 3D track with complex geometry was employed as driving environment to render the prediction task challenging. Finally, the robustness of the neural network to changes in the driver and track was investigated to set guidelines for future works.Comment: 10 pages, 9 figures, 3 tables. This work has been submitted to the IEEE Transactions on Intelligent Transportation Systems for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl