<div>We describe a new task called The Amazing Race: Robot Edition. In this task, the robot is placed in a real, unknown environment, without a map, and asked to find a designated location. It will need to explore its surroundings, find and approach people, engage them in a dialogue to obtain directions to the goal, and follow those directions to the hallway with the goal. We describe and implement a variety of robotic behaviors that performs each of these functions. We test these in the real world in test environments that were distinct from the training environments where we developed our methods and trained our models. Additionally, these test environments were completely unmodified and reflect the state of the real world.</div><div>First, we describe how our robotic system solves this problem where the environment is constrained to a single floor or a single building. We demonstrate that we are able to find a goal location in never-before-seen environments. Next, we describe a machine-learned approach to the dialogue and components of our system to make it more robust to the diversity and noisiness of navigational instructions someone may provide.</div>

Robust and timely perception of the environment is an essential requirement of all autonomous and semi-autonomous systems. This necessity has been the main factor behind the rapid growth and adoption of LiDAR sensors within the ADAS sensor suite. In this thesis, we develop a fast and accurate 3D object detector that converts raw point clouds collected by LiDARs into sparse occupancy cuboids to detect cars and other road users using deep convolutional neural networks. The proposed pipeline reduces the runtime of PointPillars by 43% and performs on par with other state-of-the-art models. We do not gain improvements in speed by compromising the network's complexity and learning capacity but rather through the use of an efficient input encoding procedure. In addition to rigorous profiling on three different platforms, we conduct a comprehensive error analysis and recognize principal sources of error among the predicted attributes. Even though point clouds adequately capture the 3D structure of the physical world, they lack the rich texture information present in color images. In light of this, we explore the possibility of fusing the two modalities with the intent of improving detection accuracy. We present a late fusion strategy that merges the classification head of our LiDAR-based object detector with semantic segmentation maps inferred from images. Extensive experiments on the KITTI 3D object detection benchmark demonstrate the validity of the proposed fusion scheme.

Neuromorphic computing has many opportunities in future autonomous systems, especially those that will operate at the edge. However, there are relatively few demonstrations of neuromorphic implementations on real-world applications, partly because of the lack of availability of neuromorphic hardware and software, but also because of the lack of availability of an accessible demonstration platform. In this work, we propose utilizing the F1Tenth platform as an evaluation task for neuromorphic computing. F1Tenth is a competition wherein one tenth scale cars compete in an autonomous racing task; there are significant open source resources in both software and hardware for realizing this task. We present a workflow with neuromorphic hardware, software, and training that can be used to develop a spiking neural network for neuromorphic hardware deployment to perform autonomous racing. We present initial results on utilizing this approach for this small-scale, real-world autonomous vehicle task.

abstract: With the growth of autonomous vehicles’ prevalence, it is important to understand the relationship between autonomous vehicles and the other drivers around them. More specifically, how does one’s knowledge about autonomous vehicles (AV) affect positive and negative affect towards driving in their presence? Furthermore, how does trust of autonomous vehicles correlate with those emotions? These questions were addressed by conducting a survey to measure participant’s positive affect, negative affect, and trust when driving in the presence of autonomous vehicles. Participants’ were issued a pretest measuring existing knowledge of autonomous vehicles, followed by measures of affect and trust. After completing this pre-test portion of the study, participants were given information about how autonomous vehicles work, and were then presented with a posttest identical to the pretest. The educational intervention had no effect on positive or negative affect, though there was a positive relationship between positive affect and trust and a negative relationship between negative affect and trust. These findings will be used to inform future research endeavors researching trust and autonomous vehicles using a test bed developed at Arizona State University. This test bed allows for researchers to examine the behavior of multiple participants at the same time and include autonomous vehicles in studies. / Dissertation/Thesis / Masters Thesis Human Systems Engineering 2019

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A lot of features that need to be scanned underground should not be disturbed, from waterlines to unmarked graves. A non-invasive way of probing underground is Ground-Penetrating Radar (GPR). GPR finds differences in materials with radar waves. However, GPR is human-operated and its position is generally determined with a GPS. In some cases, the presence of a human operator can be dangerous, and in other cases, the GPS is not reliable (i.e. mines, glaciers). Therefore there are situations where an autonomous and non-GPS-reliant solution is preferable. The current state of the autonomous GPR system targeted in this work has a non-intuitive GUI that requires an experienced hand to operate. I present an updated hardware and software platform with an intuitive GUI. This updated autonomous system continuously builds a map of its surroundings with Simultaneous Mapping And Localization (SLAM). SLAM localizes itself within the map through sensor-fused position estimates. After the survey is completed the positions are saved and integrated with radar data to be visualized. Robot Operating System 2 (ROS2) is the software I used that allows communications between hardware components, software systems, and the GUI. The new hardware package uses only one source of power and is built using quick connectors that allow for quick removal from the GPR platform. This system allows for intuitive autonomous survey planning and execution in any field paired with a simple way of visualizing data.

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Self-driving is a technology that has been envisioned in science fiction movies or in speculative design for quite some time. However, it is one of the few future technologies that is relatively easy to imagine, but very difficult to implement it into reality due to complexity coming from variability in AI. This discrepancy between reality and imagination is what makes achieving trust in self-driving vehicles more challenging, especially regarding the fact that driving is regarded as a daily task for some people. Keeping into consideration how most of the other projects done to enhance trust in automation deals with full automation, this thesis focuses how trust can be defined in semi autonomous vehicles. This middle ground setting with humans and AI systems working together needs more factors to be considered to make it autonomous, at the same time requiring a higher level of trust from drivers. An additional layer of a takeover situation from driver to AI and vice versa in a semi-autonomous setting would require more level of trust than a full self-driving vehicle where drivers do not have to control anything.Volvo Cars, an automobile manufacturer brand that has its strong focus on safety, was collaborated with in this project to support developing a notion of trust in autonomous systems. The purpose of this collaboration with Volvo Cars was to receive support in any expert knowledge in the mobility field and to create a project that is relevant to the current development state and future vision of autonomous vehicles. In order to provide an environment where drivers can calibrate trust inside vehicles, FiDO, a tangible driving assistant for building trust, was designed through a participatory design process. FiDO provides an environment for setting mutual expectation between driver and vehicle through communicating vehicle’s status and driver’s feedback with poetic visuals. FiDO learns from driver’s behaviors and their direct feedback, which provides personalized content and autonomous driving as an outcome of learning. FiDO’s usage can be adjusted based on driver’s trust level and characteristics of the service of where automation technology is used.This thesis does not cover the entire notion of trust in automation, but focuses particularly on building trust from a driver’s point of view. With including users throughout the process, this is a proof of concept how automation technology and notion of trust can be built with driver’s participation. Although detailed technological feasibility of including both humans and AI in one place to build an autonomous system were not considered into practical levels, this thesis emphasizes how we can also establish trust voluntarily from a user’s point of view.

The main research objective of this dissertation is to understand the sensing and communication challenges to achieving cooperative perception among autonomous vehicles, and then, using the insights gained, guide the design of the suitable format of data to be exchanged, reliable and efficient data fusion algorithms on vehicles. By understanding what and how data are exchanged among autonomous vehicles, from a machine learning perspective, it is possible to realize precise cooperative perception on autonomous vehicles, enabling massive amounts of sensor information to be shared amongst vehicles. I first discuss the trustworthy perception information sharing on connected and autonomous vehicles. Then how to achieve effective cooperative perception on autonomous vehicles via exchanging feature maps among vehicles is discussed in the following. In the last methodology part, I propose a set of mechanisms to improve the solution proposed before, i.e., reducing the amount of data transmitted in the network to achieve an efficient cooperative perception. The effectiveness and efficiency of our mechanism is analyzed and discussed.

Autonomous mobile robots (AMRs) use decentralized, AI-driven decision-making processes to providing material handling capabilities in industrial settings. Essay 1 examines how firms organize and engage to mitigate uncertainty during external technology integration (ETI), using an abductive approach with dyadic customer-supplier data to extend prior ETI models by exploring firm engagement, organizational adaptation, and distinct uncertainty types in AMR ETI projects. Essay 2 applies a grounded theory approach to examine AMR integration, using constant comparison and theoretical sampling to develop core categories explaining how suppliers, customers, and users exchange knowledge impacting AMR integration and project performance. Finally, Essay 3 is a conceptual paper examining the importance of end-user adoption by integrating ETI and technology acceptance model (TAM) frameworks, exploring important relationships between managerial interventions, cognitive constructs, user acceptance, and project success in AMR ETIs. As a whole, these essays contribute to the body of knowledge by extending the breadth and depth of current ETI models, emerging a substantive theory of AMR AIU, and extending TAM by grounding managerial interventions and individual cognitive constructs in an AMR context. Managers can use these frameworks to differentiate AMRs and other autonomous collaborative technology from traditional automation, and develop strategies enabling timely and effective AMR implementation.