A Review Of Instruction Methods For Collision Detection And Avoidance

Abstract

Timely detection and avoidance of collisions is crucial in both road and air transportation. Automated collision warning and avoidance systems use sensors, algorithms and actuators for collision prediction and avoidance. People must rely on their senses and mental models to detect traffic and accurately estimate collision parameters. Unfortunately, the failure to detect other traffic and to prevent imminent collisions is a major safety risk for both road and air transportation. Research on the use of traffic advisory systems in visual flight rules (VFR) flight shows that an automatically generated warning does not eliminate the safety risk by itself because many pilots select avoidance manoeuvres that are not conforming to the rules and may even be unsafe. In parallel to the spread of automated collision warning systems the training of VFR pilots must progress. This study reviews scientific studies and safety recommendations for collision detection and avoidance in flight according to visual flight rules. In addition, the study presents training aids and gives an overview of instructional methods that have been tested in experiments. Generally, research shows that specific training performed in the flight simulator is effective for improving the estimation of collision parameters (e.g., time to collision, relative distance), the decisionand performance of rule-conforming avoidance manoeuvres. Future theoretical and practical developments, including the potential benefits of virtual and augmented reality are discussed.

Keywords: Collision avoidanceinstruction methodspilot trainingvisual flight rulesvirtual realityaugmented reality

Introduction

A critical ability of people involved in vehicle control is the avoidance of loss of separation and collisions with other vehicles. Automated systems for collision warning and avoidance use sensors, algorithms and actuators for collision prediction and avoidance (Haberkorn, Koglbauer, Braunstingl, &Prehofer, 2013; Haberkorn, Koglbauer, & Braunstingl, 2014; Koglbauer, Braunstingl, & Haberkorn, 2013; Koglbauer et al., 2014; Koglbauer, Holzinger, Eichberger, & Lex, 2017). Notwithstanding the usefulness of automated aids when available, people in charge for controlling vehicles must be able to use their senses and knowledge for detecting traffic and accurately estimate collision parameters. Research shows various biases in the estimation of collision parameters such as the time to collision and relative distance between vehicles (Hancock, & Manser, 1997; Koglbauer 2015a,b; Koglbauer, Braunstingl, Haberkorn, & Prehofer, 2012; Koglbauer, Eichberger, Lex, Bliem, Sternat, Holzinger, Schinko, & Battel, 2015). Furthermore, research on collision avoidance shows that people often use heuristics for conflict resolution and that they do not always conform to the rules (Coso, Fleming, & Pritchett, 2011; Koglbauer & Braunstingl, 2018; Rantanen & Wickens, 2012).

Main Body

In air transportation the failure to detect other traffic and to prevent imminent collisions is a major safety risk (EASA, 2015; FAA, 2015). Midair collision was shown to be one of the most frequent occurrences during flight training (Lee, Bates, Murray, & Martin, 2017). An improvement of the flight training procedures for including traffic awareness and collision avoidance has been recommended by several authors (Koglbauer & Leveson, 2017; Lee et al., 2017; Shook, Bandiero, Coello, Garland, & Endsley, 2000). This study aims to provide a checklist for flight instruction on collision detection and avoidance based on scientific concepts and experimental studies. Besides the content of the instruction, the study also discusses pros and cons of the simulated and real environments that can be used for such training. Future theoretical and practical developments are discussed.

This study identifies relevant concepts, models and research relevant for the specification of a training guideline for collision detection and avoidance in flight according to visual flight rules (VFR). Using the SEEV model of selective attention (Wickens, 2015; Horrey, Wickens, & Consalus, 2006) it can be postulated that the probability of detecting traffic depends on the characteristics of the traffic salience, the effort for scanning the environment, the expectancy to encounter traffic and the value or importance of detecting traffic. As both traffic detection and collision avoidance take part in a dynamic, multitasking environment, the prediction of human performance must consider the multiple resource model (Wickens, 2002). In addition, the study discusses instructional methods for collision detection and avoidance. These methods were effective in improving the timeliness of traffic detection (Eichberger et al., 2018), the estimation of collision parameters (e.g., time to collision, relative distance), and the selection of rule-conforming avoidance manoeuvres (Koglbauer, 2015b).

Methodology

In this study qualitative methods such as task analysis and the literature review are used. The task of collision avoidance in Visual Flight Rules (VFR) flight can be divided in the following sub-tasks: scanning, traffic detection, estimation of time to collision and relative distance, decision about the conflict and VFR rules for collision avoidance depending on the type of traffic and collision geometry, action selection, execution, and evaluation. In case that the collision was successfully avoided, the pilots must return to their route. In case that the collision is still imminent the pilots must adjust their avoidance manoeuvre.

Content of the Training

Visual Scanning

The methods for visual scanning of the environment outside the cockpit and monitoring the radio communication are described in detail in the Safety Bulletin (AOPA, 2018; EGAST,2011). However, the successful detection of traffic depends not only on the scanning technique, but also on psychological factors. For improving the performance predicted by the SEEV model (Wickens, 2015) training should address the effortful head movements necessary for detecting traffic, task-sharing in multitasking situations, the expectancy to encounter traffic and the importance of detecting traffic.Anticipative information processing in flight can be trained using a method described by Koglbauer (2009). In addition, benefits and limitations of new and conventional traffic displays for VFR have been assessed during simulation experiments (Haberkorn et al., 2013; 2014).

Estimations of the time to collision and relative distance

In a flight simulator study Koglbauer (2015b) showed that student pilots initially overestimate the time to collision and relative distance to other airplanes. Thus, the student pilots that identify other traffic on collision course think that they have longer time to collision and relative distances than they really have (Koglbauer, 2015b). However, Koglbauer (2015b) showed also that when student pilots received feedback on their estimations during training in the flight simulator, they significantly improve the accuracy of their estimations.

Decision and Action

Two decisions are crucial for successful collision avoidance: decision if there is a conflict or not, and the decision about the avoidance manoeuvre. VFR pilots need not necessarily use a traffic display for detecting traffic. They can rely on their visual scanning and radio communication messages (AOPA, 2018; EGAST, 2011). The effect of traffic displays on the accuracy and timeliness of pilots’ conflict decisions was investigated by Haberkorn et al., (2014). The study showed that new displays that use predictive cues and present icons for the type of traffic, directional and relative track cues allow faster conflict decisions and are preferred by pilots as compared to conventional displays (Haberkorn et al., 2014). After the pilots decide that there is an imminent collision, or after they receive a warning from a traffic advisory system they must select an avoidance manoeuvre. Pilots’ difficulties in selecting and executing rule-conforming collision avoidance manoeuvres were demonstrated in several studies (see for example Koglbauer et al., 2013; Haberkorn et al., 2013; Wickens, Hellenberg, & Xu, 2002). Using the Systems-Theoretic Process Analysis (STPA) Koglbauer and Leveson (2017) identified potential causes and counter-measures for unsafe avoidance actions, including non-actions. However, Koglbauer (2015b) showed that student pilots significantly improve their avoidance decisions after practical training in the flight simulator. In addition, Koglbauer and Braunstingl (2018) showed that training in a network of flight simulators significantly improves the situational awareness and performance of flight students in complex traffic situations where more aircraft are involved in taxi, departure, and approach procedures. The multitasking training designed and evaluated by Koglbauer and Braunstingl (2018) can be applied effectively with ab initio student pilots at the beginning of their training program. The limited multitasking capacity of individuals is well explained by the multiple resource model (Wickens, 2002). However, research shows that training with the Variable Priority (VP) method can improve the ability of individuals to juggle several tasks (Eichberger et al., 2018; Gopher, Weil, & Siegel, 1989).This is important because research shows that skills acquired during part-task training may not transfer to a multitasking situation (Gopher et al., 1989).

Training Aids

In Table 01 an overview of training aids for collision avoidance in VFR flight is presented. The table lists advantages and disadvantages of the training aids.

Table 1 -
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Practical applications

For aiding the design of training programs, examples of exercises described in research studies are presented in Table 02 .

Table 2 -
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Conclusion

Loss of separation between aircraft is a major safety risk in air transportation (EASA, 2015; FAA, 2015). In addition to the introduction of traffic displays and warning for VFR flight an improvement of the flight training procedures for collision avoidance has been recommended (Koglbauer & Leveson, 2017; Lee et al., 2017; Shook et al., 2000).The use of simulation technology can be seen as a safer training alternative (Koglbauer & Braunstingl, 2018). This study provides a checklist for flight instruction on collision avoidance based on a review of scientific studies and safety recommendations of aviation expert groups (AOPA, 2018; EGAST, 2011).Research shows that specific training performed in the flight simulator is effective for improving the estimation of collision parameters (e.g., time to collision, relative distance), the selection and the performance of rule-conforming avoidance manoeuvres. The use of complex multitasking training scenario is recommended (Eichberger et al., 2018; Gopher et al., 1989; Koglbauer & Braunstingl, 2018). Future improvements are expected from the use of virtual and augmented reality technologies.

Acknowledgments

This study was performed as a university project and received no financial support from any external source.

References

  1. AOPA (2008).Safety Advisor Collision Avoidance. Retrieved fromhttps://www.AOPA.org/training-and-safety/online-learning/safety-advisors-and-safety-briefs/collision-avoidance, on July 22, 2018.
  2. Coso, A.E., Fleming, E.S., & Pritchett, A.R. (2011).Characterizing pilots’ interactions with the aircraft collision avoidance system. Proceedings of the 16th International Symposium on Aviation Psychology. Dayton, OH, USA, 493-498.
  3. EASA European Aviation Safety Agency (2015).Annual Safety Review 2014. Cologne, Germany: European Aviation Safety Agency.
  4. EGAST (2011).Collision Avoidance.Retrieved from https://www.EASA.europa.eu/document-library/general-publications/egast-leaflet-ga-1-collision-avoidance, onJuly 22, 2018.
  5. Eichberger, A., Koglbauer, I., & Kraut, M. (2018). Improved Perception of Motorcycles. Forschungsarbeiten des österreichischen Verkehrssicherheitsfonds. Austrian Ministry of Transportation, Innovation and Technology.
  6. FAA Federal Aviation Administration (2015).Fact sheet - General Aviation safety.[Retrieved from www.faa.gov/news/fact_sheets/news_story.cfm?newsId=19134, July 1, 2015].Farmer, E., Van Rooij, J., Riemersma, J., Jorna, P., &Moraal, J. (2003).Handbook of simulator-based training. Aldershot, England: Ashgate.
  7. Gopher, D., Weil, M., & Siegel, D. (1989).Practice under changing priorities: an approach to the training of complex skills. Acta Psychologica, 71, 147-177.
  8. Haberkorn, T., Koglbauer, I., Braunstingl, R., & Prehofer, B. (2013). Requirements for future collision avoidance systems in visual flight: a human-centered approach. IEEE Transactions on Human-Machine Systems, 43(6), 583-594.
  9. Haberkorn, T., Koglbauer, I., &Braunstingl, R. (2014).Traffic displays for visual flight indicating track and priority cues. IEEE Transactions on Human-Machine Systems, 44(6), 755-766.
  10. Hancock, P.A., & Manser, M.P. (1997). Time-To-Contact: More Than Tau Alone. Ecological Psychology, 9(4), 265-297.
  11. Horrey, W.J.,Wickens, C.D.,Consalus, K.P. (2006). Modeling drivers' visual attention allocation while interacting with in-vehicle technologies. Journal of Experimental Psychology: Applied, 12(2), 67-78.
  12. Koglbauer, I. (2009). Multidimensional approach of threat and error management training for VFR pilots: evaluation of anticipative training effects during simulated and real flight. (Dissertation Thesis). Graz, Austria: University of Graz.
  13. Koglbauer, I., Braunstingl, R., &Haberkorn, T. (2013). Modeling human and animal collision avoidance strategies.In Proceedings of the 17th International Symposium on Aviation Psychology, (pp. 554-559).Dayton, OH: Wright State University.
  14. Koglbauer, I., Eichberger, A., Lex, C., Bliem, N., Sternat, A., Holzinger, J., Schinko, Ch., & Battel, M. (2015). Bewertung von Fahrerassistenzsystemen von nicht professionellen Fahrerinnen und Fahrern im Realversuch. In C., Chaloupka-Risser (Ed.) in motion (5) Humanwissenschaftliche Beiträge zur Sicherheit und Ökologie des Verkehrs. Mehr Sicheres Verhalten im Strassenverkehr (pp. 59-70). Salzburg, Austria: INFAR.
  15. Koglbauer, I., Holzinger, J., Eichberger, A., & Lex, C. (2017). Drivers’ interaction with the Adaptive Cruise Control on Dry and Snowy Roads with Various Tire-Road Grip Potentials. Journal of Advanced Transportation, 1-10. DOI:
  16. Koglbauer, I., Braunstingl, R., Fruehwirth, K., Grubmueller, E., &Loesch, S. (2014). Gender issues in usability of glass cockpit for General Aviation aircraft. In D., Bridges, J., Neal-Smith, & A. J., Mills (Eds.) Absent Aviators: Gender Issues in Aviation (pp. 239-260). Surrey, England: Ashgate Publishing Ltd.
  17. Koglbauer, I. (2015a). Gender differences in time perception. In R., Hoffman, P. A. Hancock, M., Scerbo, R., Parasuraman& J. L. Szalma (Eds.) The Cambridge Handbook of Applied Perception Research (pp. 1004-1028). New York, NY: Cambridge University Press.
  18. Koglbauer, I. (2015b). Simulator training improves the estimation of collision parameters and the performance of student pilots. In V. Chis & I. Albulescu (Eds.) Procedia - Social and Behavioral Sciences, 209, 261-267.
  19. Koglbauer, I. (2015c). Training for prediction and management of complex and dynamic flight situations. In V. Chis & I. Albulescu (Eds.) Procedia - Social and Behavioral Sciences, 209, 268-276.
  20. Koglbauer, I., Braunstingl, R., Riesel, M., & Braunstingl, D. (2014). Ab initio flight training in a network of simulators: chances and challenges. In A. Droog (Ed.), Proceedings of the 31st Conference of the European Association for Aviation Psychology (pp. 507-510). Groningen, NL: European Association for Aviation Psychology.
  21. Koglbauer, I., Braunstingl, R., Haberkorn, T., & Prehofer, B. (2012). How do pilots interpret and react to traffic display indications in VFR flight? In A. Droog (Ed.), Proceedings of the 30th Conference of the EAAP (pp. 227-231). Groningen, NL: European Association for Aviation Psychology.
  22. Koglbauer, I., Riesel, M., & Braunstingl, R. (2016). Positive effects of combined aircraft and simulator training on the acquisition of visual flight skills. Cognition Brain Behavior. An Interdisciplinary Journal, 20(4), 309-318.
  23. Koglbauer, I., &Leveson, N. (2017).System-Theoretic Analysis of Air Vehicle Separation in Visual Flight. In M. Schwarz & J. Harfmann (Eds.), Proceedings of the 32nd Conference of the European Association for Aviation Psychology (pp. 117-131). Groningen, NL: European Association for Aviation Psychology.
  24. Lee, S.Y., Bates, P., Murray, P., & Martin, W. (2017). Training flight accidents: An explorative analysis of influencing factors and accident severity. Aviation Psychology and Applied Human Factors, 7(2), 107-113. DOI:
  25. Oberhauser, M., Braunstingl, R., Dreyer, D., & Koglbauer, I. (2018). What’s Real About Virtual Reality Flight Simulation? Comparing the Fidelity of a Virtual Reality with a Conventional Flight Simulation Environment. Aviation Psychology and Applied Human Factors, 8(1), 22-34.
  26. Rantanen, E.M., &Wickens, C.D. (2012). Conflict resolution maneuvers in air traffic control: Investigation of operational data. International Journal of Aviation Psychology, 22(3), 266–281.
  27. Shook, R.W.C., Bandiero, M., Coello, J.P., Garland, D.J., Endsley, M.R. (2000).Situation awareness problems in General Aviation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 44(1), 185-188. DOI:
  28. Wickens, C.D. (2002).Multiple resources and performance prediction. Theoretical Issues in Ergonomics Science, 3(2), 159-177, DOI:
  29. Wickens, C.D. (2015). Noticing events in the visual workplace: The SEEV and NSEEV Models. In R., Hoffman, P. A. Hancock, M., Scerbo, R., Parasuraman& J. L. Szalma (Eds.) The Cambridge Handbook of Applied Perception Research (pp. 749-768). New York, NY: Cambridge University Press.
  30. Wickens, C.D., Hellenberg, J. & Xu, X. (2002).Pilot maneuver choice and workload in free flight. Human Factors, 44(2), 171–188.

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25 June 2019

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Koglbauer, I., Baciu, C., & Braunstingl, R. (2019). A Review Of Instruction Methods For Collision Detection And Avoidance. In V. Chis, & I. Albulescu (Eds.), Education, Reflection, Development – ERD 2018, vol 63. European Proceedings of Social and Behavioural Sciences (pp. 382-388). Future Academy. https://doi.org/10.15405/epsbs.2019.06.47