The introduction of new CASs results in a multiplication of the number of different systems in the airspace. And this is not without consequences. During ACAS Xa integration tests, though the coordination was working perfectly, it was noted that the performances of TCAS were degraded. This highlighted the fact that CASs need not only to be able to coordinate but they also need to be interoperable.
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The automotive world is on the verge of a revolution with vehicles reaching higher levels of automation. Automation is defined by the Oxford English Dictionary as: “the use of electronic or mechanical devices to replace human labor”. Among all the existing concepts (e.g., Google, Uber, Tesla, but also Renault, Toyota and many more) the automation level can vary greatly. Indeed, different concepts have different goals, some of them aim at removing the human from the dynamical parts of the driving task, while others want to keep the human “in” or “on” the loop (e.g., cars with or without steering wheels). This created the need for a classification of the different goals and levels of automation, which led to the birth of the “J3016: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” document (can be bought obtained for free here). This document is the result of a common US/Europe effort to create a descriptive and informative but not normative document, which writing has been entrusted to the SAE on-road automated vehicle standards committee.
The main contribution of this document is the determination of six levels of automation, along with precise definitions for several terms.
This document divides the act of driving in three stages: strategic (e.g., choosing the route, timing), tactical (e.g., motion inside the traffic), and operational (e.g. reflex reactions). It is pointed out that the automation levels considered do not deal with the strategic part. Numerous definitions are provided, here are a few of the most useful ones with simple definitions of our own (see the actual document for precise definitions):
- Dynamic Driving Task (DDT): what most people would call driving, involves the tactical and operational parts mentioned earlier.
- Dynamic Driving Task Fallback (DDT fallback): the system that deals with the situation when things go wrong. Can be the human itself.
- Automated Driving System (ADS): the automation part of the car (hardware & software).
- Operational Design Domain (ODD): specific operational description in which an ADS will be designed to operate.
These four terms are the most used throughout the document, and in the main tables/figures. With these terms being defined, the document goes on to describe the six levels of automation that an ADS can provide. These are described in the following table.
– Level 0 = not automation at all.
– Level 1 = longitudinal or lateral distance handled autonomously.
– Level 2 = both distances handled, but human must continuously monitor the system.
– Level 3 = human does not need to monitor the system but he is the fallback system (must intervene if something wrong happens).
– Level 4 = human not needed anymore (even for fallback), but operations subjected to some limitations.
– Level 5 = like level 4 but with no operational limitations.
These six levels allow to describe precisely the levels of autonomy of existing cars and of the ones to come, for example Tesla cars are Level 2. Some more interesting concepts are detailed through the document, like the possibility to have a remote driver, or a dispatcher capable of initializing or deactivate the ADS. The monitoring (permanent vigilance) and receptive (no vigilance but can be alerted) concepts are detailed. The notion of minimal risk condition, some sort of contingency procedure, is also discussed. And more notions that would not fit in this short article.
To conclude, this document is not a standard, as it is repeated throughout the text; it is a descriptive and informative document. Yet, it is very detailed and clear, with numerous examples to illustrate the various concepts. From all the concepts, it appears that a parallel with aeronautics could be done, for example a “minimal risk condition” looks a lot like a “contingency procedure”. Defining a dictionary of equivalent notion to translate notion from one world to the other would appear highly useful. Especially as drone levels of automation are not yet clearly categorized and could use such a taxonomy, at least as a starting basis.
In the beginning of this year, the Chair researchers have submitted four papers to highly rated conferences, but also to more specialized and industry oriented ones. Letters of acceptation for the four papers have been received recently, which means that the Chair will be able to present its work in four different forums. Here are the titles and abstract of the four papers:
Are You Clear About ”Well Clear” ? (to be published in ICUAS 2018)
Abstract—Regulations from the ICAO use the term Well Clear without defining it. Now, this definition is needed to design air traffic Detect And Avoid systems. A definition is currently discussed at the ICAO level, with work on the associated Remain Well Clear (RWC) function underway at standardisation bodies level (RTCA, EUROCAE). But many members of the communities impacted by these works are not well aware of their state. To adress this lack of awareness, this paper provides three contributions. First, it derives from ICAO texts the components of a RWC function: boundaries, alerts and guidances. These are linked to essential elements required to define the Well Clear term: a start and end, the actors involved, and the expected actions. Second, it summarizes the current regulatory efforts in RTCA, EUROCAE and ICAO regarding the Well Clear and Remain Well Clear notions. Third, it proposes discussion topics to move forward. From a DAA perspective, the notion of Well Clear is key to unlock RPAS full integration, i.e. operation in all classes of airspaces. Though existing works make good progress, the ressources engaged on this topic seem insufficient when compared with the complexity and importance of the task at hand.
An Introduction to Fast Time Simulations for RPAS Collision Avoidance System Evaluation (to be published in ICRAT 2018)
Collision voidance systems are crucial for RPAS integration, yet comparing their performances remain difficult. We believe that using fast time simulations and standard evalu- ation metrics would facilitate their comparison while providing insight into their benefits. However, fast time simulations are often viewed as hard to set up and limited to large scale demonstrations. We believe even small experiments can take advantage of them with huge benefits. The aim of this work is to ease access to fast time simulations by providing explanations, examples and references to previous works and to free software. We also list commonly used evaluation metrics for collision avoidance system performance ranking. By easing the setup of fast time simulation experiments, we believe future works will be able to provide their results in a more detailed and comparable form.
UAS Operations for Retail (to be published in ICAS 2018)
The number of UAS applications is quickly increasing as technology, standards and regulation allow them. With each new application, more industrial sectors get affected, and the retail sector is already being impacted. This paper presents five UAS applications that will impact the retail sector: freight, moni- toring, guiding, delivery, and advertisement. For each application, concepts of operation are provided along with the associated technological, standard and regulatory locks. These operations are then organized along time, from earliest to latest accessible, with accompanying explanation as to why and when. It is shown that the applications most publicized are not the ones that will come first. Finally, a discussion regarding the accuracy of our forecast is proposed and leads to support the enabling of drones are provided.
Machine learning for drone operations: challenge accepted (to be published in DASC 2018)
Machine learning is among the top research topics of the last decade in terms of practicality and popularity. Though often unnoticed, machine learning guides many aspects of our lives since its introduction via the big tech companies. Its abilities rise, defeating 9-dan Go professional, their accuracy increase, enabling smooth voice recognition, adding intelligence to our daily lives. However, its development is mostly supported by high tech companies rather than the public, or regulation, who show increasing concern about its usage. Despite some reluctance, machine learning has started to appear in aviation as well. Operational improvements were among the first applications. Recently an AGE sponsored competition for data scientists resulted in the first place being awarded to a routing algorithm providing a %12 improvement in fuel consumption by learning from real flight data. Other operational issues tackled by machine learning include accurate arrival time estimation and optimal take off parameters calculation. Because it originates from robotics, a part of the aviation community is particularly inclined to use machine learning: the drone community. In their search for autonomy, researchers from this community look for ways to apply machine learning to a core feature of aircraft: the avionics. However, strict regulation could limit these uses. For example, EASA drones regulation classify operations in different categories, depending on the risk. The most risky operations require avionics to be certified, which could prove tricky for non-deterministic machine learning methods. Apart from certification issues, how machine learning could be considered in risk analysis methods is also a question of interest. In this paper, we offer to present a classification of different machine learning algorithm families and consider their fitness for certification and risks analysis. For the relevant families, we discuss the enablers and try to understand the borders that might result or prevent the use of machine learning on certified safety systems, widely referring to the AIAA Roadmap for Intelligent Systems. Similar considerations are held for systems that do not require certification, but need to be taken into account in risks analysis methods. The ultimate purpose of this paper is to highlight the existing challenges which prevent machine learning algorithms from having a wider role in drone avionics, and more generally in aviation.
The presentation of these works will be an opportunity to share the Chaire’s ideas about drones integration and to get feedback from the community through discussion. We are expecting more papers to be ready by the end of this year.
As George Bernard Shaw once said “Both the optimists and the pessimists contribute to the society. The optimist invents the aeroplane, and the pessimist the parachute”. All over the world, numerous initiatives and demonstrations have shown the multiple benefits of drone operations, that is the optimists’ work. Now the pessimists are also at work to prevent malicious use of drones.