Physical Aspects of Collision Avoidance

Operations in the Next Generation Air Transportation System (NextGen) are planned to have a reduced dependency on primary surveillance radar and use Automatic Dependent Surveillance - Broadcast (ADS-B) to provide aircraft state information to Air Traffic Control (ATC) and onboard collision detection and avoidance systems. With the ongoing integration of Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS), ADS-B is expected to become the main surveillance input to UAS collision avoidance. Traffic Alert and Collision Avoidance System (TCAS) is unable to meet requirements in terms of adaptability to future operations with reduced separation margins, addition of new user groups (e.g., UAS), and support of additional surveillance inputs. Airborne Collision Avoidance System (ACAS X) is being designed to meet these requirements and to increase surveillance, alerting, and resolution performance. The RTCA has released Minimum Operational Performance Standards (MOPS) to provide system and operational performance characteristics that ADS-B must provide. This paper analyzes recorded ADS-B data from test flights that were performed in the Continental U.S. (CONUS) and verifies measured performance with specified operational characteristics in terms of coverage, accuracy, and integrity.

PurposeThe paper aims to investigate the compatibility of manned-aircraft airborne collision avoidance systems (ACAS) for use on unmanned aerial vehicles (UAV).Design/methodology/approachThe paper uses the Fault Tree method for defining ACAS model adapted for the UAV operations, with the aim of showing the presence of certain factors that configure in such ACAS system, and whose failure can lead to an adverse event – mid-air collision.FindingsBased on the effectiveness analysis of ACAS solution adapted for the UAV operations, for given inputs, it can be concluded that the probability of ACAS failure is on the order of 10–4, as well as that in the case of autonomous ACAS solution for the UAV, the probability is reduced to 10–5. The most influential factors for the failure of the UAV’s ACAS are as follows: technical implications on the UAV, human factor, sensor error, communication and C2 link issue.Practical implicationsThe established model could be used both in the UAV’s ACAS design and application phases, with the aim of assessing the effectiveness of the adopted solution. The model outputs not only highlight the critical points of the system but also provide the basis for defining the Target Level of Safety (TLOS) for the UAV operations.Originality/valueThe developed model can be expected to speed up the design and adoption process of ACAS solutions for the UAVs. Also, the paper presents one of the first attempts to quantify TLOS for the UAV operations in the context of collision avoidance systems.

<div class="htmlview paragraph">Since 1955 the aviation community has been investigating the feasibility of an airborne collision avoidance system. These studies have included analytical and engineering design efforts as well as flight simulation tests and actual flight tests. The majority of the effort expended during these investigations has been devoted to the engineering aspects of the systems. Of equal importance in meeting the overall objective of collision avoidance is the transmission of system information to the crew in a form that can be used effectively in the operational environment. The Boeing Commercial Airplane Company fulfilled a contract for the FAA, using simulation to help determine flight deck display requirements and operational procedures for the traffic alert and collision avoidance system (TCAS II) in commercial transport aircraft.</div> <div class="htmlview paragraph">Using the experience gained from these studies, this paper describes the type of efforts required to develop pilot factor characteristics for future collision avoidance systems it addresses man-machine interface requirements to ensure that the system is compatible with the overall aircraft system. It presents some of the functional requirements that the display and control components of the system must meet to accomplish separation assurance. Some of the subjects that are discussed include: a) the impact of the collision avoidance system utilization philosophy on the display requirements; b) the importance of designing the collision avoidance display system so that it can be integrated into the flight deck environment; c) techniques for incorporating realistic human performance estimates into the system performance assumptions; d) the effect on system performance of using natural response patterns; e) techniques that can be used to generate pilot response time commensurate with the situational requirements; f) the role of simulation in the design process; and g) consideration for certification during the development of a collision avoidance system.</div>

Time/frequency technology provides a reliable aircraft collision avoidance system (CAS) that can operate in either synchronous or asynchronous modes. Precision time-ordered techniques of CAS provide both range and range-rate measurements in a one-way sense to all aircraft as well as ground stations within range of transmitted microwave signals. The cooperative system utilizes exact frequency references coupled with precise synchronization: control of frequency to 1 part in 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> and time to less than 1 µs. In addition to performing specific functions of protecting aircraft, the time/frequency CAS provides a means for wide dissemination of submicrosecond timing. Flying clocks, which are an integral part of the airborne CAS, have been providing transcontinental and intercontinental transfer of time since 1964. CAS ground stations can serve as depositories of time and frequency derived from flying clocks, satellites, Loran-C and Omega navigation systems, or television transfer referenced to national and international time/frequency standards. Airborne relay of CAS time provides a ready means for local transfer of time to users that are not within line of sight to a ground station. Thus time and frequency can be disseminated for applications other than collision avoidance. In the process of developing the technology and originating CAS, new systems for communication, position determination, navigation, and vehicle surveillance evolved. Such systems can be structured to operate at high data rates, provide improved accuracy and use less RF spectrum than existing techniques.

Automated warning and alert devices such as airborne collision avoidance systems (ACASs) represent a class of automation that is often found to be imperfect. The imperfections can be expressed as the number of false alarms or missed events. Most ACASs are constructed with a bias to prevent misses (which may have catastrophic consequences) and therefore, coupled with a low base-rate of conflict events, create high false alarm rates. In this paper, we review the adequacy of various CDTI warning algorithms that have been proposed and tested in addressing the false alarm issue, and the potential for multiple levels of alerting to mitigate the effects of false alarms on trust and reliance on the CDTI. We suggest new directions for future research, including evaluating the effects of false alarm rates on pilots' use of the CDTI, determining what strategies may enhance pilot tolerance of false alarms, and investigating the use of CDTI in conjunction with air traffic controllers.

&lt;div class="htmlview paragraph"&gt;This paper provides a summary of the data and findings from a ten-month evaluation of the Traffic Alert and Collision Avoidance System (TCAS) II Industry Prototype installed on a Piedmont Airlines' Boeing 727 aircraft. The data presented represents quantitative and qualitative information on the performance of TCAS II in the National airspace System (KAS) and identifies the impacts of TCAS on Air Traffic Control and the flight crew. The information provided concentrates on the characteristics of the TCAS encounters and the operational use of the system.&lt;/div&gt;

With the development of marine technology, the research of unmanned surface vehicles (USVs) has received wide attention from the world. Designing effective collision avoidance algorithm in dynamic sea environment is necessary for the navigation of USVs. This paper describes a collision avoidance system (CAS) with COLREGs compliance to improve autonomous navigational ability of USV. For small podded propulsion USV, a three-DOFs maneuvering model is established and the parameters of model are determined by the parameter identification method based on field experiments. About the COLREGs rules, the different maneuvering behaviors are summed up according to encounter situation and collision risk assessment. In the CAS, finite control set model predictive control is adopted, and COLREGs rules are taken as an evaluation standard in the quality function. Finally, the system is verified to avoid multiple dynamic obstacles and compliance vessel in the simulation and real environment, and the results of collision avoidance under four encounter situations are described in detail.

This research develops a performance evaluation model of humans in avoiding ship collision situations compared to the AI ship collision avoidance system (CAS). A human, the remote operator (RO) of Maritime Autonomous Surface Ships (MASS), ought to make compact collision avoidance (CA) decisions to secure safety and efficiency during remote operations. Hence, evaluating RO’s CA performance is important; however, research on developing evaluation methods concentrates merely on evaluating trainees based on instructor’s guidelines, while artificial intelligence (AI) makes CA decisions through parameter-based calculation. Therefore, this research proposes a CA performance evaluation model for RO of MASS based on CAS intervening of RO trainee’s simulation training in ship collision avoidance. In each time interval, the RO’s decisions showed divergent behaviors under given situational conditions compared to the CAS’s behaviors in equivalent situations. Findings denote the benefits of CAS intervention methods and RO performance evaluation model based on the proposed performance features.

The paper looks into how onboard aircraft technology may negatively impact the efficiency of ATC operations. It specifically notes that secondary surveillance radars (SSRs) may experience interference from systems like the Airborne Collision Avoidance System (ACAS), Automatic Dependent Surveillance-Broadcast (ADS-B), and other related systems that operate on the same frequency band. The accuracy of radar data may be compromised by this interference, making it more difficult for ATC controllers to manage and monitor air traffic efficiently. Thus, the paper provides an overview of current problems and promising solutions in the noise immunity of ATC radar systems, emphasizing the importance of continuous improvement of technologies to ensure air traffic safety.

This paper reports on the research done under a National Aeronautics and Space Administration (NASA) Research Announcement task, “Assessment of System Safety Risks for NextGen Concepts and Technologies.” We describe the methods and tools used in the task and results from first-order analyses of two NextGen ground-based automation concepts. The first-order results demonstrate the ability of the assessment method developed in previous NASA research to address NextGen concepts and to link research results rigorously to systemwide risk. The method explicitly addresses response times and failures, and the results, while first-order, specifically identify the time criticality of tactical conflict detection and resolution. I. Introduction n this research, we extended and applied to automated NextGen concepts the separation assurance safety risk assessment method we developed for the NASA Research Announcement task, “Conduct Safety Analysis of the Separation Assurance Function in Today’s National Airspace System (NAS)” (Ref. 1). As in our previous work, we addressed the task objective in two ways. First, we established functional and quantitative frameworks to allow integration of the results of all forms of safety and risk research into a single risk assessment. Second, we identified sources of data and developed and tested modeling tools to demonstrate the feasibility of our analysis approach. We conducted first-order risk assessments of two NextGen concepts: a Ground-Automation Controlled (GAC) concept based on the Advanced Airspace Concept (AAC) proposed by Erzberger in Ref. 2, and a near-term GroundAutomation Assisted (GAA) concept based on the concept proposed by McNally et al. in Ref. 3. Both concepts are based on managing traffic with ground-based automation using data link communications to control aircraft on timebased four-dimensional trajectories (4DTs). This paper describes analysis of the fully operational concepts plus two abnormal conditions. The first abnormal condition is a loss of intent data condition, which could be caused by a weather or emergency disruption leading to open 4DTs or by a hardware failure leading to loss of transmitted intent data; the second is a single aircraft inoperative transponder condition, which could be caused by hardware failure or by being turned off. Both concepts include a ground-based strategic element, a ground-based tactical element, an airborne Traffic Alert and Collision Avoidance System (TCAS) element, and an airborne see-and-avoid element. The AAC concept includes a monitoring human controller element, while the human-based GAA concept includes an active human controller in the strategic and tactical elements. The paper is organized as follows: 1) Section II presents our aviation safety risk analysis method and introduces the two NextGen concepts assessed. 2) Section III discusses the conflict modeling tool and the conflict probability data used for the risk assessments.

Collision Avoidance Systems (CASs) are attracting a lot of attention as one of the most preferred solutions for advanced driver assistance and autonomous driving. However, scene understanding, which is an essential functionality in CASs, remains a major challenge mainly due to the need for real-time understanding of highly dynamic and complex environment. In this research, a number of robust and low complexity vision based scene understanding techniques for collision avoidance on roadway have been proposed. It has been well recognized in the literature that road surface detection in a dynamic environment is both challenging and computationally intensive. An efficient non-parametric road surface detection algorithm that exploits the depth cue is proposed to overcome the limitations of existing road surface detection methods. Unlike existing methods that attempt to fit the road surface into rigid models, the proposed method results in low computational complexity, mainly due to the reliance on four intrinsic road scene attributes observed under stereo geometry. It has been demonstrated that the proposed method is capable of detecting both planar and non-planar road surfaces. Extensive experimental results using three challenging benchmarks (i.e. enpeda, KITTI stereo/flow, and Daimler) show that the proposed road surface detection algorithm outperforms the baseline algorithms both in terms of detection accuracy (up to 23.12%) and runtime performance (up to 95.00%). Next, robust and low complexity algorithm for computing the ego-vehicle’s motion state is proposed. The proposed method estimates the ego-motion of the vehicle by first employing a novel pruning technique to reduce the computational complexity of the corner feature detection process without compromising on the quality of the extracted corner features. A robust and compute-efficient KLT tracker is proposed to facilitate the generation of the feature correspondences. Finally, an early RANSAC termination condition is introduced to the Gaussian-Newton optimization scheme to achieve rapid convergence of the motion estimation process. Evaluations based on the KITTI odometry benchmark show that the proposed visual odometry method outperforms the baseline algorithms both in terms of accuracy (up to 48.36%) and runtime performance. In addition, the proposed algorithm is placed among the top 15% when evaluated using the well-known KITTI odometry platform. Methods for robust and low complexity stereo-vision based obstacle detection and tracking are proposed. Unlike the works that focus only on the detection of vehicles or pedestrians, the proposed obstacle detection method relies on u-v disparity space to detect all obstacles in the scene. A Space of Interest (SOI) is defined to greatly reduce the search space of obstacles prior to employing adaptive hysteresis thresholding and connected component labeling techniques to segment SOI into sets of obstacles. Method for tracking obstacles across frames is also proposed by constructing a distinctive object appearance model. A number of strategies to further increase the distinctiveness and reduce the computational complexity for constructing the object model are also adopted. Finally, an online multi-object tracking framework is proposed by integrating the obstacle detection and data association modules in a robust way. Evaluations using the KITTI tracking benchmark confirm that the proposed obstacle detection and tracking method outperforms the baseline algorithm in terms of tracking accuracy by up to 51.78%. In addition, compared to the baseline algorithm that achieves about 0.23 frame per second (fps), the proposed method lends well for real-time performance with 20 fps. Finally, an efficient and robust risk assessment framework is proposed by integrating the obstacle detection and tracking, and visual odometry methods proposed in this thesis. The Extended Kalman Filter is customized to enhance the robustness of the predicted trajectory of the obstacles for assessing the collision risk. The robustness of collision prediction has been enhanced by accommodating positioning uncertainty. Evaluations based on the KITTI tracking dataset demonstrate that the proposed method are capable of robust and efficient assessment of the collision risk in diverse traffic scenarios. The proposed vision based scene understanding techniques in this research have paved the way towards realizing a real-time capable collision avoidance system that is both affordable and dependable.

Mid-air collision is an important problem of modern air transport system. Development of Airborne Collision Avoidance System (ACAS) is grounded on safety criteria in order to support air traffic capacity and equipment. In research, we study the possibility and performance of airplane positioning by data accumulated in ACAS. ACAS surveillance equipment supports accurate range and poor angular measurements of airspace user’s data. An approach is based on a fusion of surveillance data with airplanes locations obtained from decoded Automatic Depended Surveillance-Broadcast messages. Performance of positioning approach is considered in more detail. An error of positioning in the horizontal plane is estimated in relation with uncertainty of airspace users’ location. Numerical demonstration with a live air traffic data indicates poor positioning accuracy in comparison with primary positioning system on-board of airplane and accuracy dependence from geometry and capacity of air traffic.

Active air traffic controllers and remotely piloted aircraft pilots were studied in a real time simulation providing results on actual and desired behaviour of the studied concept of a mid-air collision avoidance system in an air traffic control environment. The workload for the studied system seems to be acceptable at this state, and no major concerns regarding workload were found during the simulations. However, areas where the studied concept may be improved were identified concerning for instance suggested manoeuvre. A general conclusion from the simulations was that it is important that the D&amp;A-system is correctly designed in regard to sensitivity.

Cooperative collision avoidance between inland waterway ships is among the envisioned services on the Internet of Ships. Such a service aims to support safe navigation while optimizing ships' trajectories. However, to deploy it, timely and accurate prediction of ships' positioning with real-time reactions is needed to anticipate collisions. In such a context, ships positions are usually predicted using advanced Machine Learning (ML) techniques. Traditionally, ML schemes require that the data be processed in a centralized way, e.g., a cloud data center managed by a third party. However, these schemes are not suitable for the collisions avoidance service due to the inaccessibility of ships' positioning data by this third party, and allowing connected ships to get access to sensitive information. Therefore, in this paper, we design a new cooperative collision avoidance system for inland ships, while ensuring data security and privacy. Our system is based on deep federated learning to collaboratively build a model of ship positioning prediction, while avoiding sharing their private data. In addition, it is deployed at multi-access edge computing (MEC) level to provide low-latency communication to ensure fast responses during collision detection. Furthermore, it relies on Blockchain and smart contracts to ensure trust and valid communications between ships and MEC nodes. We evaluate the proposed system using a generated dataset representing ships mobility in France. The results, which demonstrate the accuracy of our prediction model, prove the effectiveness of our cooperative collision avoidance system in ensuring timely and reliable communications and avoiding collisions between ships.

Many challenges arise when attempting to use unmanned aerial vehicles (UAVs) in indoor environments, such as the lack of a GPS signal for use in navigation and the smaller margin of error in movements. Typically, those challenges are addressed by using a collision avoidance system. However, most commercially available collision avoidance systems are expensive, limited in suppliers, and are restricted to use on a specific platform. Additionally, some of the collision avoidance systems choose to forego obstacle detection in one or more directions, usually the upward direction. This work proposes that it is possible to develop a custom, low-cost collision avoidance system with modular capabilities, allowing it to be adapted to any UAV platform. The feasibility of the proposed system was determined by creating a single-direction prototype that was implemented on a small quadcopter and tested by flying the quadcopter towards a wall at slow speeds. To develop the system's control algorithm a model of a quadcopter was built. Using the model, a feedback control algorithm was developed and tested via simulation with the model. The feasibility of the proposed collision avoidance system is promising with the prototype able to prevent the quadcopter from colliding with a wall. However, further refinement in the methodology and techniques used to develop the system is needed to improve performance and reliability of the system, especially as obstacle detection is added in other directions of motion.