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Abstract Autonomous vehicles (AVs) rely heavily upon their perception subsystems to “see” the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus, it is imperative to test the vehicle extensively in all conditions which it may experience. However, the development of robust AV subsystems requires repeatable, controlled testing—while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring access to specialist facilities. Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real world being developed.

Abstract Wheel chocks are rather simple compliant mechanisms for stabilizing vehicles at rest. However, chocks must be carefully designed given the complex interaction between the chock and the tire/suspension system. Despite their importance for safety, literature is surprisingly limited in terms of what makes a wheel chock efficient. Using simple but reliable quasi-static mechanical models, this study identifies mechanical requirements that help to avoid a number of failure modes associated with many existing wheel chocks. Given that chock grounding is not always possible, a chock’s maximum restraining capacity is only obtained when the wheel is completely supported by the chock. A generic chock profile is proposed to achieve this objective while mitigating undesirable failure modes. The profile is based on fundamental mechanical principles and no assumption is made on the load interaction between the chock and the wheel.

Abstract The ground-effect problems of loss of thrust and fountain-effect instabilities are quantified. Experiments to control and augment ground-effect lift and stability are presented, including jet momentum reflection and fountain redirection using various types of internal and external underbody ventral strakes. By strategically designing the vertical takeoff and landing (VTOL) ventral surface, reflection of the impinging fountain momentum is possible so that instead of losing 10% thrust while in ground-effect, remarkably, thrust is augmented 10% or more to a considerable height above the ground, in addition to stabilizing random pitch and roll moments caused by fountain instability.

Abstract Advanced passenger vehicles are complex dynamic systems that are equipped with several actuators, possibly including differential braking, active steering, and semi-active or active suspensions. The simultaneous use of several actuators for integrated vehicle motion control has been a topic of great interest in literature. To facilitate this, a technique known as control allocation (CA) has been employed. CA is a technique that enables the coordination of various actuators of a system. One of the main challenges in the study of CA has been the representation of actuator dynamics in the optimal CA problem (OCAP). Using model predictive control allocation (MPCA), this problem has been addressed. Furthermore, the actual dynamics of actuators may vary over the lifespan of the system due to factors such as wear, lack of maintenance, etc. Therefore, it is further required to compensate for any mismatches between the actual actuator parameters and those used in the OCAP.

Abstract In this study, a computer-aided design (CAD) geometry system that is linked to each other to create a parametric form of the side rear door’s inner frame sheet piece on a passenger vehicle body in a Siemens NX environment was developed. The system was created in the NX CAD environment, using the program’s unique product development structure. The system was designed and modified for time-consuming parts. At the end of the study, the parameterized vehicle door geometries worked in the NX environment standardized the design process and accelerated the design works.

Abstract The automotive industry offers many applications for machine learning (ML), in general, and deep neural networks in particular. However, the real-world deployment of neural networks into safety-critical components remains a challenge as models would need to offer robustness under a wide range of operating conditions. In this work, we focus on uncertainty estimation, which can be used to deliver predictors that fail gracefully, by detecting situations where their predictions are unreliable. Following Gräber et al. [1], we use Recurrent Neural Networks (RNNs) to perform sideslip angle estimation. To perform robust uncertainty estimation, we augment the RNNs with generative models. We demonstrate the advantage of the proposed model architecture over Monte Carlo (MC) dropout [2] on the Revs data set [3].

Abstract The promise and potential for a future of automated vehicles (AVs) remains great, with safety and societal transformations that may rival the original introduction of the automobile. Yet an inability for industry and governments to define what it means for an AV to drive safely has tempered enthusiasm and risks causing a “winter of AV” just like the one that affected Artificial Intelligence technologies decades ago, which is only now being overcome. Towards this end, the Responsibility-Sensitive Safety (RSS) model was introduced as an open and transparent white-box, an interpretable and scalable formal model that defines minimum safety requirements based on reasonable assumptions of others, balancing safety and usefulness for automated driving vehicles.

Abstract This article presents a new machine learning (ML) development lifecycle which will constitute the core of the new aeronautical standard on ML called AS6983, jointly being developed by working group WG-114/G34 of EUROCAE and SAE. The article also presents a survey of several existing standards and guidelines related to ML in aeronautics, automotive, and industrial domains by comparing and contrasting their scope, purpose, and results.

Abstract Scenario-based testing is a promising approach to solving the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number of concrete scenarios can theoretically occur in real-world road traffic, the extraction of scenarios relevant in terms of the safety-related behavior of these systems is a key aspect for their successful verification and validation. Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. Rather than an (elaborate) rule-based assignment by extracting concrete scenarios into predefined functional scenarios, the presented method deploys an unsupervised machine learning pipeline. The approach allows for exploring the unknown nature of the data and their interpretation as test scenarios that experts could not have anticipated.

Abstract Non-pneumatic tires (NPTs) have been widely used due to their advantages of no occurrence of puncture-related problems, no need of air maintenance, low rolling resistance, and improvement of passenger comfort due to its better shock absorption. It has a variety of applications as in earthmovers, planetary rover, stair-climbing vehicles, and the like. Recently, the unique puncture-proof tire system (UPTIS) NPT has been introduced for passenger vehicles segment. The spoke design of NPT-UPTIS has a significant effect on the overall working performance of tire. Optimized tire performance is a crucial factor for consumers and original equipment manufacturers (OEMs). Hence to optimize the spoke design of NPT-UPTIS spoke, the top and bottom curve of spoke profile have been described in the form of analytical equations. A generative design concept has been introduced to create around 50,000 spoke profiles.

Abstract Threat identification and security analysis have become mandatory steps in the engineering design process of high-assurance systems, where successful cyberattacks can lead to hazardous property damage or loss of lives. This article describes a novel approach to perform security analysis on embedded systems modeled at the architectural level. The tool, called Security Threat Evaluation and Mitigation (STEM), associates threats from the Common Attack Pattern Enumeration and Classification (CAPEC) library with components and connections and suggests potential defense patterns from the National Institute of Standards and Technology (NIST) Special Publication (SP) 800-53 security standard. This article also provides an illustrative example based on a drone package delivery system modeled in AADL.

Abstract The brake discs are subjected to thermal load due to sliding by the brake pad and fluctuating loads because of the braking load. This combined loading problem requires simulation using coupled thermo-mechanical analysis for design evaluation. This work presents a combined thermal and mechanical finite element analysis (FEA) and evolutionary optimization-based novel approach for estimating the optimal design parameters of the ventilated brake disc. Five parameters controlling the design: inboard plate thickness, outboard plate thickness, vane height, effective offset, and center hole radius were considered, and simulation runs were planned. A total of 27 brake disc designs with design parameters as recommended by the Taguchi method (L27) were modeled using SolidWorks, and the FEA simulation runs were carried out using the ANSYS thermal and structural analysis tool.

Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.

Abstract Aircraft cybersecurity efforts have tended to focus at the strategic or tactical levels without a clear connection between the two. There are many excellent engineering tools already in widespread use, but many organizations have not yet integrated and linked them into an overarching “campaign plan” that connects those tactical actions such as process hazard analysis, threat modeling, and probabilistic methods to the desired strategic outcome of secure and resilient systems. This article presents the combined systems security engineering process (CSSEP) as a way to fill that gap. Systems theory provides the theoretical foundation on which CSSEP is built. CSSEP is structured as a control loop in which the engineering team is the controller of the design process. The engineering team needs to have an explicit process model on how systems should be secured, and a control algorithm that determines what control actions should be selected.

Abstract A novel geometry for a six degrees of freedom (6DOF) unmanned aerial vehicle (UAV) rotary wing aircraft is introduced and a flight mechanical analysis is conducted for an aircraft built in accordance to the thrust vectors of the proposed geometry. Furthermore, the necessary mathematical operations and control schemes are derived to fly an aircraft with the proposed geometry. A system identification of the used propulsion system with the necessary thrust reversal in the form of bidirectional motors and propellers was conducted at a whirl tower. The design of the first prototype aircraft is presented as well as the first flight test results. It could be demonstrated that an aircraft with the thrust vectors oriented according to the proposed geometry works sufficiently and offers unique maneuvering capabilities that cannot be reached with a conventional design.

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