Basics of qualitative research: Grounded theory procedures and techniques

How to ... be a reflective practitioner.

Intelligent Manufacturing in the Context of Industry 4.0: A Review

Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way for creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.

Industrial Internet of Things: Challenges, Opportunities, and Directions

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

A Review of Motion Planning Techniques for Automated Vehicles

We present an autonomous driving research vehicle with minimal appearance modifications that is capable of a wide range of autonomous and intelligent behaviors, including smooth and comfortable trajectory generation and following; lane keeping and lane changing; intersection handling with or without V2I and V2V; and pedestrian, bicyclist, and workzone detection. Safety and reliability features include a fault-tolerant computing system; smooth and intuitive autonomous-manual switching; and the ability to fully disengage and power down the drive-by-wire and computing system upon E-stop. The vehicle has been tested extensively on both a closed test field and public roads.

/pdf/towards-a-viable-autonomous-driving-research-platform-3e3km603on.pdf

Towards a viable autonomous driving research platform

As the complexity of software for Cyber-Physical Systems (CPS) rapidly increases, multi-core processors and parallel programming models such as OpenNP become appealing to CPS developers for guaranteeing timeliness. Hence, a parallel task on multi-core processors is expected to become a vital component in CPS such as a self-driving car, where tasks must be scheduled in real-time. In this paper, we extend the fork-join parallel task model to be scheduled in real-time, where the number of parallel threads can vary depending on the physical attributes of the system. To efficiently schedule the proposed task model, we develop the task stretch* transform. Using this transform for global Deadline Monotonic scheduling for fork-join real-time tasks, we achieve a resource augmentation bound of 3.73. In other words, any task set that is feasible on m unit-speed processors can be scheduled by the proposed algorithm on m processors that are 3.73 times faster. The proposed scheme is implemented on Linux/RK as a proof of concept, and ported to Boss, the self-driving vehicle that won the 2007 DARPA Urban Challenge. We evaluate our scheme on Boss by showing its driving quality, i.e., curvature and velocity profiles of the vehicle.

Parallel scheduling for cyber-physical systems: analysis and case study on a self-driving car

Traditional mechanical subsystems in automobiles are being replaced by electronically controlled systems, often with no mechanical backup. This trend towards "drive-by-wire" systems is becoming increasingly popular. In these cyber-physical systems, a critical task not meeting its timing deadline can lead to a safety violation and damage to life and/or property. Classical real-time scheduling techniques such as RMS and EDF can be used to guarantee the schedulability of periodic tasks. However, certain critical tasks like the engine control task are activated by engine events such as pulses generated by sensors at the engine crankshaft. The periods of these engine tasks vary continually and even dramatically depending on the engine speed. The conventional periodic task model is inadequate for handling such tasks in cyber-physical systems due to its pessimism when combined with common schedulability analyses. In this paper, we define a new task model called Rhythmic Tasks for tasks having periods that vary due to external physical events. To the best of our knowledge, this is the first model that considers continually varying periods for fixed-priority scheduling in dynamic operating environments. We formally define the rhythmic task model and study its scheduling properties. In the context of rhythmic engine control tasks, we offer schedulability tests for determining the maximum possible utilization under the steady state, which is related to the physical engine speed. We also investigate the range of possible engine acceleration and deceleration rates. We show that excessive acceleration and deceleration can make the system unschedulable. We provide algorithms to find the appropriate ranges for acceleration and deceleration rates. We use a specific case study of engine control to illustrate our analysis.

Rhythmic Tasks: A New Task Model with Continually Varying Periods for Cyber-Physical Systems

Current approaches to personalization either presuppose people's needs and automatically tailor services or provide formulaic options for people to customize. We propose a complementary approach to personalization: a reflective strategy that helps people realize what matters to them and enables them to better personalize services themselves. To design this strategy, we first studied the practices of eight personal health service providers. We then tested the strategy's efficacy by building a Fitbit Plan website that encouraged Fitbit users to customize a plan or accept an automatically tailored plan. For one group of users, the website used the reflective strategy to assist in the plan setup process. A two-week between-subjects field experiment showed that the reflective strategy helped motivate users to carry out their plans, increasing their average daily steps by 2,425 steps. Without the reflective strategy, users either set easy goals or failed to carry out system-created plans, ultimately showing no change in their average daily steps. This work suggests that helping people reflect on and connect with their own goals in using a personalized service could advance the effectiveness of the service.

Personalization revisited: a reflective approach helps people better personalize health services and motivates them to increase physical activity

Autonomous driving technologies have been emerging over the past few years, and semi-autonomous driving functionalities have been deployed to vehicles available in the market. Since autonomous driving is realized by the intelligent processing of data from various types of sensors such as LIDAR, radar, camera, etc., the complexity of designing a dependable real-time autonomous driving system is rather high. Although there has been much research on building a reliable real-time system using hardware replication, the resulting systems tend to add significant extra cost due to hardware replication. Therefore, an alternative solution would be helpful in building an autonomous vehicle in a cost-effective way. An autonomous driving system is different from the conventional reliable real-time system because it requires (1) flexible design, (2) adaptive graceful degradation and (3) effective use of different modalities of sensors and actuators. To address these characteristics, we summarize SAFER (System-level Architecture for Failure Evasion in Real-time applications) our previous work on flexible system design. We then present a conceptual framework for autonomous vehicles to provide adaptive graceful degradation and support for using different types of sensors/actuators when a failure happens. We motivate our proposed framework with various scenarios, and we describe how SAFER can be extended to support the proposed conceptual framework.

Junsung Kim

Papers

Towards a viable autonomous driving research platform

Parallel scheduling for cyber-physical systems: analysis and case study on a self-driving car

Rhythmic Tasks: A New Task Model with Continually Varying Periods for Cyber-Physical Systems

Personalization revisited: a reflective approach helps people better personalize health services and motivates them to increase physical activity

Towards dependable autonomous driving vehicles: a system-level approach