Academic literature on the topic 'Cockpit voice and data recorder (CVDR)'

Aircraft are complex systems that rely heavily on monitoring and real-time communications with the base station. During aviation and flight operations, diverse data are gathered from different sources, including the Cockpit Voice Recorder (CVR), Flight Data Recorder (FDR), logbook, passenger data, passenger manifest etc. Given the high sensitivity of flight data, it is an attractive target for adversaries which could result in operational, financial and safety related incidents. Communications between aircraft pilots and air traffic controllers are all unencrypted. The data, mainly audio communication files, are placed openly within data centers on the ground stations which could lead to a serious compromise in security and privacy. One may rely on the cloud owing to its on-demand features but to thwart possible attacks, the data need to be encrypted first, giving rise to the issue of conducting search over encrypted data. This research presents a novel approach for data security in aviation industry by introducing a semantic-based searchable encryption scheme over the cloud. The designed system has proven to be extraordinarily effective for semantic-based searchable encryption at the word and the text level. The rigorous security and complexity analysis shows that the proposed solution provides a high level of security and efficiency and can be effectively deployed in the aviation sector. The designed scheme is tested through a real-world aviation dataset collected to demonstrate the significance of this research. The proof of concept proves to be secure, privacy-preserving and lightweight while resisting distinguishability attacks.

ABSTRACTThrough an analysis of TAWS/FMS data collected and registered during the last minute of TU-154M aircraft flight from Warsaw to Smolensk, we show the value and existence of space for undertaking research works on enhancing standard functionality of TAWS/FMS systems to enable their effective use in examination of the course and causes of air accidents. The flight ended up in the total destruction of the aircraft and the death of all passengers and crew on board. The TAWS/FMS flight altitudes and spatio-temporal data, i.e. geographical location and speed of the aircraft motion, were inspected for their internal and external consistency with the data from the ATM QAR service recorder. Using the data from ATM QAR, records from the cockpit voice recorder (CVR), jointly with data from the TAWS/FMS systems, it was possible to reconstruct the most probable horizontal and vertical trajectory of the TU-154M aircraft during the last minute of flight before its complete destruction, as well as the likely scenario of accompanying events. The data available from recorders enabled the authors to gain information on the preliminary stage of the course of accident, and the first phase of the aircraft's destruction, resulting in serious damage of the left wing of the aircraft. Enhanced standard functionality of TAWS/FMS systems, incorporating use of their data in the post-accident situations examination, would have improved results and simplified the present analysis considerably.

Theoretical basis This case has two primary purposes. First, it allows students to examine how cognitive bias can affect decision making in stressful situations. Students explore why individuals make flawed choices. They learn about how managers shape the context and the process through which teams make decisions. For instance, automation can create a climate in which people then struggle to cope with the unexpected when it happens. Students examine why individuals make these systematic errors in judgment. The case demonstrates that leaders need to be aware of the traps that individuals and teams encounter when they make decisions in crisis situations, and it enables students to discuss the strategies that leaders can employ to avoid these traps. Second, the case provides an opportunity to examine a catastrophic failure in detail. Students discover that it can be nearly impossible to identify a single factor that caused the failure. Instead, they learn how to apply multiple theoretical perspectives to examine a serious organizational breakdown. They become familiar with important concepts from behavioral decision theory, such as complex systems theory and how it interacts with cognitive bias. Research methodology The technical report released by the French Aviation Authority along with the primary flight cockpit voice recorder data were used as the basis for this case. Other available public data such as news reports were used to round out the case study. Case overview/synopsis On June 9, 2009, on a routine flight from Rio de Janeiro to Paris, Air France 447 (AF 447), carrying 220 people crashed in the mid-Atlantic Ocean. Drawing from various first-hand accounts (cockpit voice recorder) and secondary evidence of the tragedy, the case provides a detailed account of the key events that took place leading up to the accident. The case describes how the pilots on AF447 were confronted with a scenario they had not faced before, and through the confusion made a series of errors. Through many of the quotes in the text, readers gain an understanding of the impressions and perceptions of the pilots, including how they felt about many of the critical decisions and incidents during the last minutes of the flight. The case concludes by highlighting the main findings of the BEA report. Complexity academic level This case study is appropriate for undergraduate students studying organizational behavior. It is also appropriate for MBA-level leadership and behavior classes.

In Canada, cockpit voice recorders (CVRs) are only mandated for a specific category of aircraft, resulting in many commercial and business aircraft not being equipped with these crucial devices. More recently, industry initiatives have led to the development of new technologies enabling carriers to install lightweight and relatively affordable flight recorders on aircraft that are not currently covered by CVR requirements. Many of these non-conventional recorders have capabilities that meet or even exceed those of conventional CVRs by relying on high-resolution audio and imagery recordings, infinite cloud storage, data links, etc. Canadian legislation bestows a statutory privilege on the contents of CVRs installed in accordance with regulations, but it remains unclear if this privilege also applies to non-conventional flight recorders. Through case studies, this article analyses the design and functionalities of existing devices on the market, revealing that some recorders are not actually CVRs within the meaning of the law and/or suffer from technical vulnerabilities that preclude their contents from being privileged. This article concludes by outlining the resulting liability risks associated with the use of non-conventional recorders and recommends that regulations be issued to approve eligible devices based on a fourprong test, which attests whether their recordings can benefit from the statutory privilege afforded to conventional CVRs.

Background: The number of aircraft-assisted suicides can only be considered a rough estimate because it is difficult and, at times, impossible to identify all cases of suicide. Methods: Four recent reports of accidents occurring in 1997 in Indonesia, 1999 in Massachusetts in the United States, 2013 in Namibia, and 2015 in France related to commercial aircraft-assisted suicides were analyzed. This analysis relied on data extracted from the accident reports that supported aircraft-assisted suicide from the: (a) cockpit voice recorder (CVR) and flight data recorder (FDR), (b) medical history, (c) psychosocial history, (d) toxicology, (e) autopsy, and (f) any methodology that utilized aviation medicine. There are some limitations in this study. Although all analyzed accident investigations followed ICAO Annex 13 guidelines, there is variability in their accident investigations and reporting. In addition, accident investigation reports represent accidents from 1997 to 2015, and during this time, there has been a change in the way accidents are reported. The nature of this analysis is explorative. The aim was to identify how the various aircraft accident investigators concluded that the accidents were due to suicidal acts. Results: In all four accident reports, FDR data were available. CVR data were also available, except for one accident where CVR data were only partially available. Comprehensive medical and psychosocial histories were available in only one of four of the accident reports. Conclusion: To prevent accidents involving commercial aircraft, it is necessary to identify the causes of these accidents to be able to provide meaningful safety recommendations. A detailed psychological autopsy of pilots can and likely will assist in investigations, as well as generate recommendations that will substantially contribute to mitigating accidents due to pilot suicide. Airborne image recording may be a useful tool to provide additional information about events leading up to a crash and thus assist in accident investigations.

Literature New capabilities to modernize the U.S. National Airspace System (NAS) include support of real-time information streams derived from many data sources across the NAS. As an emergent property, safety of the NAS arises from interactions between many elements at different levels, ranging from those attributable to humans, technology, and the environment. Each component in the NAS needs to interact with other components, exchange resources and information, and operate under broad regulations to achieve overall system objectives (Harris & Stanton, 2010). Sometimes, incidents and accidents result from insufficient interaction (communication and coordination) between humans (e.g., pilot-controller). The content of communication provides value and supports understanding with a multitude of individual, group, team, and data sets within air traffic research. In addition, another dimension to communication with a potentially rich source of understanding is everything outside of its explicit meaning. Cooke and Gorman (2009) describe methods of communication flow between teams (considered to be a system) that have proven insightful. The first is a ratio of team members speech quantity, which can indicate the degree of influence one member has over others. Another is the communication required and passed score, or how much variation there is in actual team communication from expectations. Flow quantity represents how much speech each member of the team produces. Gorman et al.’s (2012) study applied discrete Recurrence Quantification Analysis (RQA) to team communication flow data in order to visualize and measure coordination dynamics of Unnamed Aircraft Vehicle (UAV) teams, both mixed teams (i.e., team members changed) and intact teams (i.e., team members stayed the same over successive experimental sessions). Interestingly, mixed teams were better able to adjust to unexpected perturbations; this ability was linked to team level coordination dynamics. That is, mixed teams adopted a globally stable pattern of communication while exhibiting strong temporal dependence (Gorman, Cooke, Amazeen, & Fouse, 2012). Similarly, Demir, Cooke, & Amazeen (2018) applied discrete RQA on human-robot interaction in an Urban Search and Rescue task and multivariate extension of RQA on human-synthetic team in a UAV task. They underline that metastable team coordination (not too stable nor too flexible) between team members is associated with the ability to successfully overcome novel events (i.e., team situation awareness) in a dynamic task environment. The current project addresses the question of how human factors related to air traffic control (ATC), specifically situation awareness and cognitive load, interact with other factors in the NAS to affect ATC performance and a result in a safe and effective NAS? One way to answer this question is focusing on ATC-pilot communication as a chief performance indicator. In the current study, we investigate the potential of dynamical systems perspectives to capture the differential dynamics of three cases between controller-pilot communication flow during incidents and accidents. Method One of the approaches for investigating interaction patterns between system components (in the controller-pilot case) and their change over time involves looking at communication flow using discrete Recurrence Plot (RP) and corresponding Recurrence Quantification Analysis (RQA), which quantifies how many recurrences with a certain length are present by multidimensional space (phase space) trajectory in a dynamical system (Marwan, Carmen Romano, Thiel, & Kurths, 2007). RP is the basis of discrete RQA (Eckmann, Kamphorst, & Ruelle, 1987), which is a visual tool for demonstrating a system’s recurrent structure in the phase space when a system revisits specific states or sequences of states within a region of phase space over a period of time. In the case of two or more systems, discrete RP displays the times when two or more separate dynamical systems show a recurrence simultaneously (Marwan et al., 2007). Three cases of controller-pilot audio transmissions with their communication time stamps were obtained from “Cockpit Voice Recorder Transcripts” (2019), visualized using RP, and analyzed via discrete RQA. The cases represent situations of particular interest, communication, and coordination. Discrete RQA quantifies not only the effect of interventions (such as unexpected events) on instability, but also the dyad interaction processes and the dynamics that contribute to that process. The RQA was used to produce several measures, including percent recurrence rate, percent determinism (DET), longest diagonal line, longest vertical line, entropy, and laminarity. Of these, the focal variable was determinism (Marwan et al., 2007), which indicates the amount of organization in the communication of a system. DET is derived from the recurrence plot by examining how the recurrent points are distributed. Dyads with high determinism tend to repeat sequences of states many times, while a controller-pilot with low determinism rarely repeats a sequence of states, producing few diagonal lines. Results and discussion One of the objectives of this study is to monitor human performance indicators in real-time in the NAS to make predictions about risk. The current exploratory paper presents an idea about how to model human interaction between two or more roles with the larger purpose of developing NAS risk prognostics. We have presented three controller-pilot communication flows via discrete RP and RQA methods that differentiate three real cases based on discrete interaction sequences. The measures extracted from the RQA and visualizations of the interaction patterns show that effective communication and coordination is needed for effective situation awareness, i.e., overcoming the failures. Based on previous studies (Demir et al., 2018), we expected that the rigidity of the coordination dynamics between controller and pilot in one of the cases would associated with a fatal accident as well as lack of communication (confusion during the landing), resulting in a lack of situation awareness. On the other hand, two other incidents demonstrated more flexible behavior across the roles (controller-pilot) to adapt to the dynamic environment. In this case, the key lies in the dynamic transition between interaction and the environment. The controller and pilot are compelled to adjust their interaction patterns (flexibility) to adapt to changes in the environment and maintain a stable trajectory toward meeting their goals, such as landing safely. Thus, there are three crucial states for effective interaction in both temporal and spatial states: what needs to be communicated, when it needs to be coordinated, and how it needs to be communicated and coordinated”.

Concern has grown in recent years over the number of airline accidents and incidents for which investigators were unable to retrieve cockpit voice recorder (CVR) data. In 2018, a National Transportation Safety Board (NTSB) report listed 34 events in which “pertinent CVR data were overwritten” and called for the introduction of CVRs with a 25-hour recording duration. In 2021, the European Union introduced regulations that require new transport aircraft weighing over 27,000 kg to be equipped with 25-hour CVRs. Building on a previous study (Cookson, 2019), this paper presents a protocol for examining safety events for which CVR data were unavailable, and examines 52 safety events that occurred between 2014 and 2022. In a majority of cases, the pertinent CVR data were overwritten because there was no prompt deactivation of the CVR after the safety event and/or a delay in notifying the investigating agency. The paper highlights a lack of standardization in the way that CVR data are presented in accident and incident reports, and a worrying subset of cases with a minimal description of CVR data or no mention at all.

Integrating Artificial Intelligence (AI) into aviation incident-accident investigations presents unique opportunities and significant challenges. This paper explores the complexities of incorporating AI into the aviation investigation process, emphasizing the importance of a human-centric approach to ensure the technology's reliability, transparency, and accountability. The application of AI in investigations necessitates thorough adherence to existing international frameworks, including ICAO Annex 13 and regulatory guidelines from the Federal Aviation Administration (FAA), the European Union Aviation Safety Agency (EASA), and the National Transportation Safety Board (NTSB). However, AI provides improved data analysis, predictive modeling, and pattern recognition capabilities. Through the examination of crucial case studies, such as the investigation into the Lion Air Flight 610 and Ethiopian Airlines Flight 302 (Boeing 737 MAX) accidents, we illustrate how AI-driven data analytics helped investigators to analyze large quantities of flight data recorder (FDR) and cockpit voice recorder (CVR) information (FAA, 2024). AI-based systems contributed to investigating the Air France Flight 447 accident (Airbus A-330), where advanced data analysis techniques provided insights into pilot responses under adverse conditions (Stewarts, 2017). These case studies highlight AI's strengths and limitations in understanding complex system failures and human-machine interactions.Moreover, these examples underscore the necessity of human oversight in interpreting AI outputs and ensuring accurate, context-driven conclusions. Considering regulatory differences, the research findings address the intricate challenges of harmonizing AI systems with established human-led investigative methodologies. Specifically, the research focuses on how AI can be effectively integrated without compromising the critical decision-making processes traditionally managed by human investigators.Furthermore, the presented research examines how human factors must be prioritized to prevent over-reliance on AI outputs, maintain investigative integrity, and foster cross-disciplinary collaboration between AI experts and aviation safety professionals. By analyzing these case studies and providing a comprehensive review of AI's role in modern aviation safety, the research team aims to illuminate the path toward developing AI frameworks that complement human expertise rather than replace it. Ultimately, this paper calls for a balanced approach that leverages AI's strengths while addressing its limitations, ensuring that future aviation incident-accident investigations remain human-centered and safety-focused.