Applying Gaming Technology to Tomahawk Mission Planning and Training

Fall 2005 Simulation Interoperability Workshop, Paper Number 4 & Presentation.Simulation Interoperability Standards Organization (SISO) SIW Conference PaperOver the past decade the computer gaming industry has not only generated its own multi-billion dollar section of the entertainment industry, but it has also made significant inroads into the military market, especially in training and simulation, starting with Marine Doom and continuing up to today ’s Full Spectrum Command and America ’s Army. This paper describes a Navy-funded research project that uses gaming technology for not only training, but also as an operational decision aid for the Tactical Tomahawk Weapon Control System (TTWCS). The research is aimed at adapting game engine technology to predict and simulate the motion of ground target vehicles (e.g. SCUD Launchers) through their local terrain over a given period of time, then use the associated rendering capabilities to provide realistic 3D views. The paper presents an overview of the TTWCS mission and how it will benefit from specific advances in gaming technology, especially in the areas of artificial intelligence, path finding, and physics. It discusses the current state of the project using existing commercial gaming technology and the future plans for adapting and expanding the open source game engine technology of the Delta3D project underway at the MOVES Institute at the Naval Postgraduate School

Prediction Model for Offloading in Vehicular Wi-Fi Network

It cannot be denied that, the inescapable diffusion of smartphones, tablets and other vehicular network applications with diverse networking and multimedia capabilities, and the associated blooming of all kinds of data-hungry multimedia services that passengers normally used while traveling exert a big challenge to cellular infrastructure operators. Wireless fidelity (Wi-Fi) as well as fourth generation long term evolution advanced (4G LTE-A) network are widely available today, Wi-Fi could be used by the vehicle users to relieve 4G LTE-A networks. Though, using IEE802.11 Wi-Fi AP to offload 4G LTE-A network for moving vehicle is a challenging task since it only covers short distance and not well deployed to cover all the roads. Several studies have proposed the offloading techniques based on predicted available APs for making offload decision. However, most of the proposed prediction mechanisms are only based on historical connection pattern. This work proposed a prediction model which utilized historical connection pattern, vehicular movement and driver profile to predict the next available AP.  The proposed model is compared with the existing models to evaluate its practicability

Simulation Of Vehicular Movement in VANET

In the recent years research in the field of vehicular ad-hoc network(VANET) is done extensively. VANET consist of large number of dynamically nodes which are vehicles over a area. Different types of technology and applications are being developed for the VANET . So this VANET technology and applications should be thoroughly checked before deployment in the real world environment. But to test technologies and applications in real world environment is not feasible it involves lot of danger and safety issues, different reports of the testing can’t also be generated so to overcome these limitation we need to carry out simulation of VANET in the computer environment i.e. we should do a computer simulation. Computer simulation is risk and danger free, we can generate different scenario (rural, urban, collision of vehicles) of the VANET using this. So computer simulation is very important in VANET research. Simulation of VANET is divided into two part a. Traffic simulation: Generation of traffic movement, Defining the mobility model for vehicle and creating traffic movement. b. Network simulation: Generating Inter communicating vehicle , Defining communication protocols. And both the simulation are connected in bi-directional coupling

보행자 거동 및 운전자 주행 특성 기반의 자율주행 종방향 거동 계획

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 이경수.본 연구는 보행자의 미래 거동 방향에 대한 불확실성을 고려한 보행자 모델을 제안하고, 보행자 대응 시의 운전자 주행 특성을 반영하여 자율주행 차량의 종방향 모션을 계획하는 알고리즘을 제시한다. 도심 자율 주행을 가능하게 하기위해서는 보행자와의 상호적인 주행이 필수적이다. 그러나, 보행자는 거동 방향 전환이 쉽게 일어나기 때문에 미래 거동을 예측하기가 어렵고, 이에 대응하는 자차의 거동을 결정짓는 데도 어려움이 있다. 이러한 보행자의 거동 불확실성이 존재함에도 자율 주행 차량이 보행자의 안전성을 확보하고 휴먼 운전자와 같이 거동하기 위해서는, 보행자의 거동 불확실성을 반영하는 보행자 모델이 우선적으로 필요하다. 해당 연구에서는 보행자 거동 특성을 조사하여 보행자 거동 확률 모델을 정의하고, 보행자 대응 상황에서의 운전자의 거동을 조사하여 자율주행 차량의 종방향 거동 계획에 적용한다. 해당 논문은 크게 보행자 모델 정의, 예측 기반 충돌 위험 평가 그리고 보행자 대응 종방향 거동 계획의 세 가지 주요 파트로 이루어져 있다. 첫 번째 파트에서 보행자 모델 정의의 핵심 이론은 보행자의 거동 속도와 방향을 전환하는 거동 사이에는 특정 상관관계를 가지고 있다는 것이다. 보행자의 거동 특성은 자율 주행 차량에 부착된 라이다 센서와 전방 카메라를 통해 획득한 보행자 데이터를 통계적으로 분석한 결과로 도출되었다. 해당 데이터를 통해 속도에 따라 보행자가 모든 방향에 대해서 거동할 확률이 도출되고, 보행자의 미래 거동 범위는 도출된 확률 분포에서 유효 시그마 범위를 설정하여 구획된다. 이는 보행자가 일정 시간 동안 특정 확률로 거동할 영역을 고려하여, 위험이 존재할 수 있는 보행자에 대해서 미리 차량의 움직임을 계획할 수 있도록 한다. 두 번째 파트로 보행자와 자 차량의 일정 시간 동안의 위치 정보를 예측하여 충돌 위험성을 평가한다. 보행자 예측은 앞서 도출한 보행자 유효 예측 거동 범위 내에서 가장 위험성이 큰 방향으로 움직인다고 가정한다. 또한, 자 차량의 경우 주어진 로컬 경로를 따라 움직인다는 가정을 하는 차선 유지 모델을 사용한다. 예측 결과를 통해 현재 추가적인 감속도를 가하지 않았을 때, 충돌 위험이 존재하는지 확인한다. 마지막으로, 타겟이 되는 보행자에 대한 종방향 거동을 결정한다. 우선적으로 보행자 대응 상황에서 적절한 감속도와 감속 시점을 결정하기 위해 휴먼 운전자 주행 데이터를 분석한다. 이를 통해 주행에서 핵심적인 파라미터들이 정의되고, 해당 파라미터들은 종방향 거동 계획에 반영된다. 따라서 최종적으로 보행자 예측 거동 영역에 대해서 자율 주행 차량의 추종 가속도이 결정된다. 제시된 알고리즘은 실차 테스트를 통해 성능이 확인된다. 테스트 결과, 도출한 보행자 모델과 예측 모델을 바탕으로 한 감속 결정 시점과 감속도의 궤적이 동일 상황들에 대해서 능숙한 운전자와 유사함이 확인되었다.This paper presents a pedestrian model considering uncertainty in the direction of future movement and a human-like longitudinal motion planning algorithm for autonomous vehicle in the interaction situation with pedestrians. Interactive driving with pedestrians is essential for autonomous driving in urban environments. However, interaction with pedestrians is very challenging for autonomous vehicle because it is difficult to predict movement direction of pedestrians. Even if there exists uncertainty of the behavior of pedestrians, the autonomous vehicles should plan their motions ensuring pedestrian safety and respond smoothly to pedestrians. To implement this, a pedestrian probabilistic yaw model is introduced based on behavioral characteristics and the human driving parameters are investigated in the interaction situation. The paper consists of three main parts: the pedestrian model definition, collision risk assessment based on prediction and human-like longitudinal motion planning. In the first section, the main key of pedestrian model is the behavior tendency with correlation between pedestrians speed and direction change. The behavior characteristics are statistically investigated based on perceived pedestrian tracking data using light detection and ranging(Lidar) sensor and front camera. Through the behavior characteristics, movement probability for all directions of the pedestrian is derived according to pedestrians velocity. Also, the effective moving area can be limited up to the valid probability criterion. The defined model allows the autonomous vehicle to know the area that pedestrian may head to a certain probability in the future steps. This helps to plan the vehicle motion considering the pedestrian yaw states uncertainty and to predetermine the motion of autonomous vehicle from the pedestrians who may have a risk. Secondly, a risk assessment is required and is based on the pedestrian model. The dynamic states of pedestrians and subject vehicle are predicted to do a risk assessment. In this section, the pedestrian behavior is predicted under the assumption of moving to the most dangerous direction in the effective moving area obtained above. The prediction of vehicle behavior is performed using a lane keeping model in which the vehicle follows a given path. Based on the prediction result, it is checked whether there will be a collision between the pedestrian and the vehicle if deceleration motion is not taken. Finally, longitudinal motion planning is determined for target pedestrians with possibility of collision. Human driving data is first examined to obtain a proper longitudinal deceleration and deceleration starting point in the interaction situation with pedestrians. Several human driving parameters are defined and applied in determining the longitudinal acceleration of the vehicle. The longitudinal motion planning algorithm is verified via vehicle tests. The test results confirm that the proposed algorithm shows similar longitudinal motion and deceleration decision to a human driver based on predicted pedestrian model.Chapter 1. Introduction 1 1.1. Background and Motivation 1 1.2. Previous Researches 3 1.3. Thesis Objective and Outline 5 Chapter 2. Probabilistic Pedestrian Yaw Model 8 2.1. Pedestrian Behavior Characteristics 9 2.2. Probability Movement Range 11 Chapter 3. Prediction Based Risk Assessment 13 3.1. Lane Keeping Behavior Model 15 3.2. Subject Vehicle Prediction 17 3.3. Safety Region Based on Prediction 19 Chapter 4. Human-like Longitudinal Motion Planning 22 4.1. Human Driving Parameters Definition 22 4.1.1 Hard Mode Distance 23 4.1.2 Soft Mode Distance and Velocity 23 4.1.3 Time-To-Collision 23 4.2. Driving Mode and Acceleration Decision 25 4.2.1 Acceleration of Each Mode 25 4.2.2 Mode Selection 26 Chapter 5. Vehicle Test Result 28 5.1. Configuration of Experimental Vehicle 28 5.2. Longitudinal Motion Planning for Pedestiran 30 5.2.1 Soft Mode Scenario 32 5.2.2 Hard Mode Scenario 35 Chapter 6. Colclusion 38 Bibliography 39 국문 초록 42Maste

Collision avoidance algorithm for intelligent vehicles using ITS-G5

Dissertação para obtenção do Grau de Mestre em Engenharia Informática e de ComputadoresDada a importância de melhorar a segurança rodoviária, é extremamente importante a criação de mecanismos que auxiliem a redução do número de acidentes rodoviários. Posto isto, esta tese visa o desenvolvimento de um algoritmo para alerta de colisões veiculares, capaz de apoiar o condutor na sua tarefa. O algoritmo proposta, inicialmente utiliza ITS-G5 como canal de comunicação entre veículos. No entanto, dada a necessidade de disponibilizar a aplicação à maioria dos utilizadores, apresenta-se também uma solução para smartphone que permite a utilização da mesma a utilizadores que não tenham acesso a uma OBU ITS-G5. Neste trabalho, o algoritmo de previsão de colisão foi definido e implementado e, para dar suporte à aplicação móvel, foi desenvolvido um ambiente híbrido entre ITS-G5 e redes celulares. A troca de informação é feita utilizando mensagens CAM. Dado a importância da latência no desempenho da aplicação, foram realizadas medições de latência para validar a solução proposta. Em geral, os melhores resultados foram obtidos aquando da utilização do 4G, obtendo uma latência média de 42 ms. O uso da rede 5G não alcançou as expectativas, pois esperavam-se menores latências que o 4G, mas apenas se verificaram latências similares e com maior instabilidade. Ao testar o algoritmo desenvolvido, foi utilizado um Digital Twin para auxiliar os testes à solução permitindo a criação de situações específicas de alto risco. Posto isto, a sua utilização foi útil para testar uma situação constante de mensagens recebidas com latência elevada originando uma situação em que a aplicação assume um cenário distinto da realidade que impede a deteção da colisão, quando a mesma existe (falso negativo).Given the importance of improving road safety, it is crucial to create mechanisms that reduce road accidents. Therefore, this thesis aims the development of a collision warning algorithm capable of supporting the drivers’ tasks by alerting them to possible collisions. The algorithm proposed, initially uses ITS-G5 as a communication channel between vehicles. However, given the need to provide the application to most users, a smartphone application is also presented, making this solution accessible to users that don’t have access to an ITS-G5 OBU. In this work, a collision warning algorithm was defined and implemented and, to support the smartphone application, a hybrid environment between ITS-G5 and cellular network was developed. Information is exchanged among players using CAM messages. Given the role that latency plays in the performance of this kind of application, latency measurements were performed to validate the proposed framework. In general, the best results were obtained when using 4G, obtaining an average of 42 ms in latency. The usage of the 5G network didn’t meet the expectations since it promises lower latencies than 4G, but similar latencies and greater instability have been observed at this stage. When testing the developed application, a digital twin was used to assist the algorithm tests allowing the creation of specific dangerous situations. Therefore, the digital twin was used to test a specific scenario of constant high latency messages creating a false negative situation, where a collision would be detected if latency was lower/admissible.N/

Дослідження ефективності використання нейронних мереж при прогнозуванні прибуття поїздів на технічні станції

For efficient management of the railway direction, it was proposed to create a predictive model of the train operation. One of the components of this model is the train arrival module, designed to determine the arrival time of different trains to technical stations of the railway direction. The train arrival module is proposed to build based on a neural network, which using statistical information for prior periods and the train data obtained in real time, determines the train arrival time at the technical station.Since the train departure parameters (time and date of departure from the next technical station, train weight and engine type) have different measurement units and there are significant differences between the minimum and the maximum value of the same parameter, it was decided to encode data about train in binary form. The values of each factor were grouped by intervals of a certain value.As a result of experiments with different types of neural networks, it was found that using the perceptron, the structure and construction method of which is given in the paper provides the smallest error of the results obtained. The operation principle of such neural network is as follows. Train information is encoded and fed to the neural network input in binary form; the result of the neural network operation is also a binary output vector, the value of which is interpreted in a certain value of the train movement duration. Based on the movement duration values, the predicted arrival time of freight trains at the technical station is calculated.Experiments with the interval value at binary coding of individual factors have shown a significant effect of this parameter on the neural network operation quality and train arrival forecasting accuracy. Для определения моментов прибытия грузовых поездов на технические станции разработан модуль прибытия, как один из составляющих прогнозной модели поездной работы направления. Модуль построен на базе нейронной сети, которая на основе статистической информации за предыдущие периоды и данных о поезде, полученных в режиме реального времени, определяет момент прибытия поезда на станцию.Для визначення моментів прибуття вантажних поїздів на технічні станції розроблено модуль прибуття, як один із складових прогнозної моделі поїзної роботи напрямку. Модуль побудований на базі нейронної мережі, яка на основі статистичної інформації за попередні періоди та даних про поїзд, отриманих в режимі реального часу, визначає момент прибуття поїзда на станцію

Implementasi Routing Protocol AODV Dengan Prediksi Pergerakan Kendaraan Dalam Vanet

AODV merupakan salah satu routing protocol yang populer karena proses route discovery-nya yang efisien. Namun jika diterapkan pada lingkungan VANET yang memiliki topologi dinamis dan berubah dengan cepat, AODV kurang mampu menjaga kestabilan dalam komunikasi antar kendaraan. Hal ini tentu disebabkan oleh AODV yang pada dasarnya diciptakan untuk lingkungan MANET. Agar dapat bekerja lebih baik di lingkungan VANET, maka diperlukan modifikasi pada routing protocol AODV. Modifikasi yang akan dilakukan adalah pada proses route request, yaitu dengan memperhitungkan faktor pergerakan kendaraan dan kualitas komunikasi antar kendaraan. Kemudian dilakukan prediksi terhadap faktor-faktor tersebut untuk beberapa detik yang akan datang. Dari hasil perhitungan tersebut akan diputuskan node mana saja yang dipilih sebagai node yang akan melanjutkan paket route request. Dari uji coba yang dilakukan, AODV-PNT mengalami peningkatan pada nilai rata-rata packet delivery ratio, penurunan rata-rata delay dan routing overhead seiring dengan bertambahnya kepadatan kendaraan dibandingkan dengan AODV ======================================================================================================== AODV is one of the popular routing protocol because of its efficiency in route discovery process. However if applied in VANET environment which topology is dynamic and changing rapidly, AODV is less able to maintain its stability in vehicle-to-vehicle communication. This is certainly due to AODV which is basically created for MANET environment. In order to work better in VANET environment, it's necessary to modify AODV routing protocol. The part that will be modified is route request process, namely by taking the movement information and link quality between vehicles into account. Then do a prediction based on those factors for possible future values. From the calculation, it will be decided which node is selected as the node that will relay the route request packet. The simulation results show that AODV-PNT is able to achieve better routing performance in packet delivery ratio, average endto-end delay, and routing overhead with increasing vehicle density compared to AOD

Implementasi Routing Protocol AODV Dengan Prediksi Pergerakan Kendaraan Dalam VANET

AODV merupakan salah satu routing protocol yang populer karena proses route discovery-nya yang efisien. Namun jika diterapkan pada lingkungan VANET yang memiliki topologi dinamis dan berubah dengan cepat, AODV kurang mampu menjaga kestabilan dalam komunikasi antar kendaraan. Hal ini tentu disebabkan oleh AODV yang pada dasarnya diciptakan untuk lingkungan MANET. Agar dapat bekerja lebih baik di lingkungan VANET, maka diperlukan modifikasi pada routing protocol AODV. Modifikasi yang akan dilakukan adalah pada proses route request, yaitu dengan memperhitungkan faktor pergerakan kendaraan dan kualitas komunikasi antar kendaraan. Kemudian dilakukan prediksi terhadap faktor-faktor tersebut untuk beberapa detik yang akan datang. Dari hasil perhitungan tersebut akan diputuskan node mana saja yang dipilih sebagai node yang akan melanjutkan paket route request. Dari uji coba yang dilakukan, AODV-PNT mengalami peningkatan pada nilai rata-rata packet delivery ratio, penurunan rata-rata delay dan routing overhead seiring dengan bertambahnya kepadatan kendaraan dibandingkan dengan AODV. ===================================================================================================== AODV is one of the popular routing protocol because of its efficiency in route discovery process. However if applied in VANET environment which topology is dynamic and changing rapidly, AODV is less able to maintain its stability in vehicle-to-vehicle communication. This is certainly due to AODV which is basically created for MANET environment. In order to work better in VANET environment, it's necessary to modify AODV routing protocol. The part that will be modified is route request process, namely by taking the movement information and link quality between vehicles into account. Then do a prediction based on those factors for possible future values. From the calculation, it will be decided which node is selected as the node that will relay the route request packet. The simulation results show that AODV-PNT is able to achieve better routing performance in packet delivery ratio, average endto-end delay, and routing overhead with increasing vehicle density compared to AOD