An infrared source with spatially and temporally variable radiance is designed. It can be used to simulate any other infrared source simultaneously in two wavelength bands. The theoretical analysis of the real and simulator source is performed to define the design parameters. A series of the concepts are evaluated for their potential as infrared sources. A three-dimensional heat transfer computer program is used to predict the thermal behavior of the prototype glassy carbon waffle target. Tests are performed on this target which show that its thermal and radiation properties are in agreement with its predicted behavior. Glassy carbon waffle source is a good infrared radiator which can be used repeatedly at high temperatures. Measurements are described which show that the uniformity in the surface temperature can be maintained even when a scanning laser beam is used to deposit the energy on the target surface. The target surface is described analytically as a low pass filter. Its time constant is shown to depend on the target material and the temperature distribution in the target.

To provide a basis for the development of interaction curves for tubular beam-columns of annular cross section, a general purpose beam-column computer program is developed, and used to determine ultimate load capacities. The paper presents the analytical model and the computer method. The analytical results are compared with published test data as well as experimental data obtained as part of this project.

Autonomous Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the safety and reliability of these systems, extensive testing is being conducted before their future mass deployment. One approach is to test ADSs directly on the road, but it is incredibly costly to cover all rare corner cases. Thus, a popular complementary approach is to evaluate an ADS’s performance in a simulator. Such method is called simulation based testing. However, randomly testing ADSs in simulation is still not efficient enough and the testing results might not transfer to the real-world. This dissertation underscores that the cornerstone of efficient simulation testing lies in crafting optimal testing scenarios. We delineate several pivotal properties for these scenarios: they should induce ADS misbehavior, exhibit diversity, manifest realism, and adhere to user specified rules (e.g., following traffic rules). Subsequent to this identification, our research delves into methodologies to enhance one or more of these properties of the generated scenarios. Specifically, we embark on two distinct lines of approach. First, we develop advanced search strategies to unearth diverse scenarios that provoke ADS to misbehave. Second, we harness the potential of deep generative models to produce scenarios that are both realistic and in compliance with user specified rules. Because of the need for efficiently testing end-to-end behaviors of ADSs against complex, real-world corner cases, we propose AutoFuzz, a novel fuzz testing technique, which can leverage widely-used driving simulators’ API grammars to generate complex driving scenarios. In order to find misbehavior-inducing scenarios, which are very rare, we propose a learning based search method to optimize AutoFuzz. In particular, our method trains a neural network to select and mutate scenarios sampled from an evolutionary search method. AutoFuzz shows promises in efficiently identifying traffic violations for the given ADSs under test. Although AutoFuzz is good at finding violations, as a black-box method, it is agnostic of the cause of the violations. In the second project, we focus on finding violations caused by the failure of fusion component, which fuses the inputs of multiple sensors and provides the ADS a more reliable understanding of the surroundings. In particular, we identify that the fusion component of an industry-grade ADAS can fail to trust the more reliable input sensor and thus lead to a collision. We define misbehavior caused by such a failure as "fusion error". In order to efficiently find fusion errors, we propose a fuzzing framework, named FusED, that uses a novel evolutionary-based search method with objective promoting fusion output to deviate from sensor input. We show that FusED can efficiently reveal fusion errors for an industry-grade ADAS. One issue with the generated scenarios by AutoFuzz or FusED (or any other search based methods) is that all the NPC vehicles are controlled by some low-level controllers, whose behaviors are different from human drivers. This poses a difficulty in transferring the found violations into real world. Some recent work tries to address this problem by using deep generative models. However, the scenarios cannot be easily controlled which is not desirable for users to customize the testing scenarios. As both realism and controllability of the generated traffic are desirable, we propose a novel method called Controllable Traffic Generation (CTG) that achieves both properties simultaneously. In order to preserve realism, we propose a conditional, dynamic enforced diffusion model. In order to satisfy controllability, we propose using a kind of "traffic language" called Signal Temporal Logic (STL) to specify what we want in traffic scenarios (e.g., following road rules). We then leverage STL to guide the conditional diffusion model for generating realistic and controllable traffic. Although CTG can generate realistic and controllable traffic, it still requires domain expertise to specify the STL based loss function. Besides, it models traffic participants independently, resulting in sub-optimal agents interaction modeling. In order to address these issues, we developed CTG++ which enables a user to use language to generate realistic traffic scenario. In particular, we proposed to use GPT4 to translate a command in natural language into a loss function in code. We then use the loss function to guide a scene-level diffusion model, which considers all the vehicles jointly, to generate traffic satisfying the command. We have found that CTG++ can generate query (in natural language)-compliant and realistic traffic simulation. In summary, our four projects discussed in this thesis have solved important problems in efficiently testing ADSs and have had significant influence in the advancement of ADS. Besides, the models and empirical studies we performed can be applicable to other testing and behavior generation problems, such as general ML-based software testing, and multi-agent behavior planning and prediction. I hope this thesis can serve as an inspiration to anyone who is interested in the exciting field of ADS testing and development, and contribute to the realization of the full automation of driving.

Current research on thermal effects on guideways has addressed many aspects of the behavior of guideways using two-dimensional models. The two-dimensional models are acceptable for existing guideway designs, in which cross sectional shapes are uniform along the length of the guideway. However, three-dimensional models are necessary for a modular design, in which the track structures that interact with Maglev vehicles are made separately and are assembled into the support structure, and in which the cross sectional shapes are not uniform. A three-dimensional numerical model of the thermal environment, in which the effect of partial shading is taken into account, is implemented for the study of guideway behavior under various thermal environments. The numerical model of the thermal environment is calibrated to the experimental results under the thermal environment at Austin, Texas, and is extrapolated to predict the behaviors of guideways under the thermal environment in Las Vegas, Nevada, which is one of the candidate sites for the implementation and deployment of the high speed Maglev transportation system. This study addresses the suitability of a modular steel guideway design under such a thermal environment. Characteristics of the behavior of guideways under various thermal environments are identified, and the behavior of guideways under the effect of partial shading is summarized. / text