Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Functional options and design concepts

학위논문 (석사) -- 서울대학교 대학원 : 공학전문대학원 응용공학과, 2021. 2. 박우진.Many studies are being conducted on technologies directly related to the commercialization of automated driving vehicles and the enactment of related laws. Additional studies, however, are insufficient. In particular, automated driving vehicles do not need human driving, so unlike conventional vehicles, the drivers obtain the freedom to do other activities inside the vehicle. The aim of this paper is to collect previous research data and to predict and analyze various activities expected from the passengers of automated driving vehicles. Also, this paper investigates and analyzes the functions and arrangements of passenger plane and ship seats, as well as the current vehicle seats. Then, it proposes the functions and features of seats required by automated driving vehicles. In addition, it checks the validity of the predictions by comparing them with automated driving concept vehicles submitted to motor shows and CES by the global automakers, the Tier1, and 2 automotive suppliers. Patents for applications, registrations, and prospective registrations were investigated to identify trends in automated driving vehicle seat technology. It also draws out what is different from the current vehicle seats in automated driving vehicle seats. It was intended to suggest the direction to move forward in the research and development of seats for automated driving vehicles. And this paper not only proposed the function and features of seats of automated driving vehicles that have not yet been commercialized, but also considered possible problems during commercialization and suggested solutions to them. The upcoming automated driving vehicle regulation trends have been investigated to confirm the validity of this paper. Based on this, additional studies needed for commercialization of automated driving vehicles were considered.자율주행차의 상용화를 위해서 직접적으로 연관된 기술과 관련 법규 제정에 관한 연구는 많이 진행되고 있다. 하지만 그 외에 부수적인 연구들은 미흡한 실정이다. 특히 자율주행차는 인간이 직접 운전을 할 필요가 없기 때문에 기존의 차와는 다르게 실내에서 다양한 활동들을 할 것으로 예상하고 있다. 따라서 기존 연구자료들을 수집하여 자율주행차 에서 기대되는 승객들의 다양한 활동들에 대하여 예측하여 보고, 현재 차량용 시트만이 아니라 여객기와 여객선의 시트들의 기능과 배치를 조사하고 분석하여, 자율주행차에서 필요한 시트의 기능과 배치를 제안하고자 한다. 또한 현재까지 모터쇼와 CES에 출품된 자율주행차의 시트의 기능과 배치를 분석하여 논문에서 제안한 기능과 배치의 타당성을 확인하여 보았고, 현재까지 출원 및 등록된 자율주행차 시트 관련 특허 조사를 통하여 자율주행차의 시트에서 기존 차량의 시트와는 다르게 중점을 두고 있는 부분이 무엇인지를 확인해보고 앞으로 자율주행차의 시트 연구 및 개발에서 나아가야할 방향을 제시하고자 하였다. 그리고 본 연구는 아직 상용화 되지 않은 자율주행차의 시트의 기능과 배치를 제안으로만 그치지않고 상용화 시에 발생할 수 있는 문제점에 대해서도 고민하고 그에 대한 해결방안을 제시하였다. 마지막으로 다가오는 자율주행차에 대응한 전세계의 차량 법규 트랜드를 조사하여 현재 차량 법규의 틀에서는 벗어난 본 연구의 자율주행차의 실내환경 및 시트를 연구의 타당성을 확인하였으며, 이 타당성을 근거로 자율주행차 상용화를 위하여 향후 필요한 추가 연구들에 대하여 고민하여 보았다.Abstract i Contents iii List of Tables v List of Fugures vii 1. Introduction 1 1.1 Study background 1 1.2 Purpose of research 4 1.3 The composition of thesis 6 2. Research for Functional Requirement of Automated Driving Vehicle Seating 8 2.1 Method 8 2.2 In-Vehicle activities research 11 2.2.1 In-Vehicle activities on public transportation 13 2.2.2 In-Vehicle activities of automated driving vehicles. 14 2.3 Vehicle seat research 14 2.3.1 Automotive seat function research 15 2.3.2 Benchmarking research 23 3. Prediction in Automated Driving Vehicle Seating 26 3.1 Prediction in Automotive Seat Function, at Each Level of Driving Automation (SAE J3016) 26 3.2 Prediction in Automotive Seat Function from Chapter.2 27 3.3 Automated Driving Concept Vehicle Research 31 3.4 Automated Driving Seat Patent Research 37 4. Problems with Commercialization of Automated Driving Vehicle Seating 41 4.1 Motion Sickness (Car Sickness) 41 4.2 Seat Variation in Response to Various Passenger Scenarios (Validation and Verification Issue) 45 5. Discussion and Conclusions 51 5.1 Limitations 51 5.2 Discussion and future works 52 Babliography 54 Abstract (In Korean) 60Maste

A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Why would people want to travel more with automated cars?

The use of automated vehicles (AVs) may enable drivers to focus on non-driving related activities while travelling and reduce the unwanted efforts of the driving task. This is expected to make using a car more attractive, or at least less unpleasant compared to manually driven vehicles. Consequently, the number and length of car trips may increase. The aim of this study was to identify the main contributors to travelling more by AV. We analysed the L3Pilot project’s pilot site questionnaire data from 359 respondents who had ridden in a conditionally automated car (SAE level 3) either as a driver or as a passenger. The questionnaire queried the respondents’ user experience with the automated driving function, current barriers of travelling by car, previous experience with advanced driving assistance systems, and general priorities in travelling. The answers to these questions were used to predict willingness to travel more or longer trips by AV, and to use AVs on currently undertaken trips. The most predictive subset of variables was identified using Bayesian cumulative ordinal regression with a shrinkage prior (regularised horseshoe). The current study found that conditionally automated cars have a substantial potential to increase travelling by car once they become available. Willingness to perform leisure activities during automated driving, experienced usefulness of the system, and unmet travel needs, which AVs could address by making travelling easier, were the main contributors to expecting to travel more by AV. For using AVs on current trips, leisure activities, trust in AVs, satisfaction with the system, and traffic jams as barriers to current car use were important contributors. In other words, perceived usefulness motivated travelling more by AV and using AVs on current trips, but also other factors were important for using them on current trips. This suggests that one way to limit the growth of traffic with private AVs could be to address currently unmet travel needs with alternative, more sustainable travel modes