This paper proposes a new strategy for collision avoidance system leveraging Time-to-Collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating a deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to traditional TTC -based approaches.

Aircraft collision avoidance systems is critical to modern aviation. These systems are designed to predict potential collisions between aircraft and recommend appropriate avoidance actions. Creating effective collision avoidance systems requires solutions to a variety of technical challenges related to surveillance, decision making, and validation. These challenges have sparked significant research and development efforts over the past several decades that have resulted in a variety of proposed solutions. This article provides an overview of these challenges and solutions with an emphasis on those that have been put through a rigorous validation process and accepted by regulatory bodies. The challenges posed by the collision avoidance problem are often present in other domains, and aircraft collision avoidance systems can serve as case studies that provide valuable insights for a wide range of safety-critical systems.

Ensuring safe and efficient operation of collaborative robots in human environments is challenging, especially in dynamic settings where both obstacle motion and tasks change over time. Current robot controllers typically assume full visibility and fixed tools, which can lead to collisions or overly conservative behavior. In our work, we introduce a tool-aware collision avoidance system that adjusts in real time to different tool sizes and modes of tool-environment interaction. Using a learned perception model, our system filters out robot and tool components from the point cloud, reasons about occluded area, and predicts collision under partial observability. We then use a control policy trained via constrained reinforcement learning to produce smooth avoidance maneuvers in under 10 milliseconds. In simulated and real-world tests, our approach outperforms traditional approaches (APF, MPPI) in dynamic environments, while maintaining sub-millimeter accuracy. Moreover, our system operates with approximately 60% lower computational cost compared to a state-of-the-art GPU-based planner. Our approach provides modular, efficient, and effective collision avoidance for robots operating in dynamic environments. We integrate our method into a collaborative robot application and demonstrate its practical use for safe and responsive operation.

Space debris poses a significant threat, driving research into active and passive mitigation strategies. This work presents an innovative collision avoidance system utilizing event-based cameras - a novel imaging technology well-suited for Space Situational Awareness (SSA) and Space Traffic Management (STM). The system, employing a Stack-CNN algorithm (previously used for meteor detection), analyzes real-time event-based camera data to detect faint moving objects. Testing on terrestrial data demonstrates the algorithm's ability to enhance signal-to-noise ratio, offering a promising approach for on-board space imaging and improving STM/SSA operations.

Designing a safe, trusted, and ethical AI may be practically impossible; however, designing AI with safe, trusted, and ethical use in mind is possible and necessary in safety and mission-critical domains like aerospace. Safe, trusted, and ethical use of AI are often used interchangeably; however, a system can be safely used but not trusted or ethical, have a trusted use that is not safe or ethical, and have an ethical use that is not safe or trusted. This manuscript serves as a primer to illuminate the nuanced differences between these concepts, with a specific focus on applications of Human-AI teaming in aerospace system control, where humans may be in, on, or out-of-the-loop of decision-making.

One of two firefighting helicopters that collided in midair over a Southern California brush fire lacked an electronic warning device that alerts pilots to approaching aircraft -- a critical deficiency, according to at least one former wildland fire pilot. As the National Traffic Safety Board continues to investigate the fatal, Aug. 6 crash of two contract California Department of Forestry and Fire Protection helicopters, a career pilot and advocate for collision avoidance systems is calling attention to the fact that one of the choppers lacked a traffic collision-avoidance system, or TCAS, which audibly alerts pilots when another aircraft is nearby. "I'm frankly shocked that this is not required on contract helicopters to this day," said Juan Browne, a former U.S. Forest Service lead plane pilot who now flies Boeing 777s out of Los Angeles for a major airline. "That's the one last piece of safety equipment that could have prevented this accident," he said. The helicopter crash, which killed three, marks a rare instance in which an aviation battle of a California fire has resulted in a midair collision.

The Traffic Alert Collision Avoidance System (TCAS) has been an integral part of the increased safety of air transport since it was federally mandated in the 1991 for all passenger carrying aircraft with more than 30 seats flying in U.S. airspace [1, 2]. TCAS led to a dramatic reduction in the occurrence of mid air collisions in modern aviation; however the heuristic based approach undertaken in TCAS has made it difficult to adapt the system to the evolving complexity of the National Airspace System (NAS), which includes new cooperative surveillance systems (e.g., ADS-B) and new vehicle entrants. In response, the Federal Aviation Administration (FAA) commissioned the development of a replacement for TCAS. This new system, referred to as the Next Generation Airborne Collision Avoidance System X (ACAS X), which is currently in development at MIT Lincoln Laboratory and John Hopkins Applied Physics Laboratory, is expected to integrate into multiple aircraft platforms and reduce nuisance alerts as well as reduce the risk of Near Mid Air Collisions (NMAC) [3]. ACAS X introduced several variants designed to reduce the risk of NMAC for a particular operation, such as commercial aviation (ACAS Xa) [4], large uncrewed aerial systems (ACAS Xu) [5], smaller uncrewed aerial vehicles (ACAS sXu) [6], and ACAS Xr which is under development for advanced air mobility and helicopter operations. Each variant adds capabilities and design considerations for the operational environment and platforms that will be commonly seen by the ACAS X equipped vehicle. For example, ACAS sXu introduced vehicle to vehicle surveillance to accommodate a future link that sUAS may use to interrogate and coordinate with each other. While, ACAS Xu added Remain Well Clear alerting due to its use in remotely piloted or autonomous UAS.

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Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applications such as autonomous driving, failures cannot be completely eliminated due to the complex stochastic environment in which the system operates. As a result, safety validation is not only concerned about whether a failure can occur, but also discovering which failures are most likely to occur. This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation. We consider a general black box setting for partially observable and continuous-valued systems operating in an environment with stochastic disturbances. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system, making it suitable for black-box testing of large systems. We present different formulations depending on whether the state is fully observable or partially observable. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where we are concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where a prototype aircraft collision avoidance system is stress tested to find the most likely scenarios of near mid-air collision.

Validation is a key challenge in the search for safe autonomy. Simulations are often either too simple to provide robust validation, or too complex to tractably compute. Therefore, approximate validation methods are needed to tractably find failures without unsafe simplifications. This paper presents the theory behind one such black-box approach: adaptive stress testing (AST). We also provide three examples of validation problems formulated to work with AST.