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... first separate the perception and decision-making functionalities, each of which can be further separated into multiple layers of abstraction for testing purposes. The differentiating factors of our approach are given in Figure 2. The 'Separation of Concerns' principle is used to decompose the problem. ...
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... it is important to note the critical contribution of this methodology, which is the testing strategy focusing on a particular layer and the cascaded analysis of these layers for the overall validation. The second component in Figure 2 is the 'Definition of Correctness.' When we use the separation of concerns, the scenarios are going to be defined in a scenario description language. ...
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... top two components in Figure 2 create the domain that allows using test generation and formal verification techniques for validation. We use constrained-random test generation to create scenarios. ...
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... existing accident databases and the data records from test tracks can be used to create abstract test scenarios. There Another important component given in Figure 2 is the 'Severity of Error.' We define a probabilistic error function to describe the severity level of the error. ...
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... first separate the perception and decision-making functionalities, each of which can be further separated into multiple layers of abstraction for testing purposes. The differentiating factors of our approach are given in Figure 2. The 'Separation of Concerns' principle is used to decompose the problem. ...
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... it is important to note the critical contribution of this methodology, which is the testing strategy focusing on a particular layer and the cascaded analysis of these layers for the overall validation. The second component in Figure 2 is the 'Definition of Correctness.' When we use the separation of concerns, the scenarios are going to be defined in a scenario description language. ...
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... top two components in Figure 2 create the domain that allows using test generation and formal verification techniques for validation. We use constrained-random test generation to create scenarios. ...
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... Furthermore, efforts are underway to develop autonomous systems for PAVs. A validation framework and testing regime that uses modeling and simulation to validate autonomous Cyber-Physical Systems (CPS) with a focus on aerial vehicles are presented by [22]. Challenges for aerial traffic management (ATM) are presented by the development and widespread adoption of PAVs, including integration with: existing air traffic control systems, dedicated airspace management, real-time monitoring, communication, data sharing, and regulation. ...
Aircraft designed for transporting one or two persons, much like a small personal car but flying in the air based on Bernoulli Principle and Newton’s laws, are called Personal Aerial Vehicles (PAVs). Due to high PAV traffic densities and the high velocities at which PAVs fly, manual piloting of PAVs is seldom recommended. Hence, PAVs are equipped with an inbuilt Autonomous Navigation and Control System (ANCS), which frees the rider from piloting skills. Machine learning (ML)-based approaches that require datasets used during training phases can be used for implementing the software of such ANCS. The development of simulation-driven systems for ANCS offers many advantages, particularly reducing the systems’ developmental costs and infrastructure needs. In this article, we report on the development of a synthetic visual dataset that enables ML-based implementation of ANCS. The state-of-the-art simulator AirSim is used to generate this dataset. Additionally, to make the synthetic data more realistic, several augmenting technologies such as Unreal Engine (3D graphics gaming), Blender animator, PX4-Autopilot SITL (Software in the loop) software, QGroundControl Autopilot App, and ROS (robot operating system) middleware suite are utilized. We also discuss the applicability of this dataset in realizing the ANCS module for PAVs.
... This allows for the sharing of comparable and reproducible scenarios. However, the complexity of autonomous system V&V scenario proposes a high dimensional problem where exhaustive testing is infeasible [6]- [8] which presents a need for optimization of the testing process. Recent approaches to this problem is the synchronized parallel testing of falsification scenarios [9] and the breakdown of scenarios into simpler, atomic scenarios to reduce the total number of testing required [10], [11]. ...
... Equation (7) finds the average distance between all points. Equation (8) finds the root of the sum of the inverse distances squared. ...
... The second level of simulation will be used to model detailed characteristics of the system such as the communication, mobility, traffic patterns etc. For this level, we plan to integrate a network simulator with a traffic simulator that can support both aerial and ground vehicles [27][28][29]. The focus of this part of the validation study will be determining the impact of realistic communication, mobility and traffic patterns on the system performance. ...
View Video Presentation: https://doi.org/10.2514/6.2021-2359.vid Urban air mobility (UAM) is anticipated to result in a multitude of sustained flight operations in metropolitan areas around the globe in the coming decades. As presently envisioned, these operations will take place squarely in the urban boundary layer (UBL), a new environment for sustained aviation operations. Due to the heterogeneity of the surface underlying the UBL and the roughness elements within it, along with other complex processes, the UBL is a spatially and temporally dynamic environment. Due to these characteristics and the new aircraft being developed to fly within it, this flight environment will have to be meteorologically resolved at fine scales. The potential of crowdsourced data has only begun to be realized in weather forecasting but may be especially well suited for this purpose. This paper proposes an overarching architecture for a cyber-physical urban meteorological observational system to support burgeoning UAM operations.
