Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles <s> TOPIC (b) Decision making framework for IAVs <s> UCAV is an inevitable trend of the future intelligent and uninhabited flight platform. Situation assessment (SA) is an effective method to solve the problem of the autonomous decision-making in UCAV investigation. The concepts, contents and process of SA are put forward and the methods about the implementation of SA are analyzed. Then the concept and inference of dynamic Bayesian networks (DBN) are introduced, and SA configuration of UCAV autonomous decision system is given. Finally, the SA is applied to the UCAV autonomous decision system, especially SA based on DBN is used and the model is propounded. The simulation result indicates that the inference results are consistent with the theoretical analysis. The subjectivity of the assessment is reduced and the accuracy is greatly improved. <s> BIB001 </s> Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles <s> TOPIC (b) Decision making framework for IAVs <s> In recent years, unmanned aerial vehicles have received a significant attention in the research community, due to their adaptability in different applications, such as surveillance, disaster response, traffic monitoring, transportation of goods, first aid, etc. Nowadays, even though UAVs can be equipped with some autonomous capabilities, they often operate in high uncertainty environments in which supervisory systems including human in the control loop are still required. Systems envisaging decision-making capabilities and equipped with flexible levels of autonomy are needed to support UAVs controllers in monitoring operations. The aim of this paper is to build an adjustable autonomy system able to assist UAVs controllers by predicting mental workload changes when the number of UAVs to be monitored highly increases. The proposed system adjusts its level of autonomy by discriminating situations in which operators’ abilities are sufficient to perform UAV supervision tasks from situations in which system suggestions or interventions may be required. Then, a user study was performed to create a mental-workload prediction model based on operators’ cognitive demand in drone monitoring operations. The model is exploited to train the system developed to infer the appropriate level of autonomy accordingly. The study provided precious indications to be possibly exploited for guiding next developments of the adjustable autonomy system proposed. <s> BIB002
Since in the future the Intelligent Autonomous vehicles (IAVs) are going to move freely in the cities, countryside, sea, space or any area without a direct human control; the IAVs are going to have their own responsibilities and therefore have to make their own decisions. Cross a road, for example, means to make a decision. The car has to choose between stop to look for other ones crossing (cars or pedestrians) or to continue. Decision making for an Autonomous Vehicle is the process by which it makes a choice between different options or possible ways to solve different situations for its performance in different contexts. In other words, the decision making framework set the conditions to identify a problem and select an action course to solve it according to predefine goals. Based on the literature reviewed here, we state a decision making general framework of what it would mean for an autonomous vehicle to be able to make a decision. Making a decision would depend on several variables, such as, the ones shown in the decision making framework. Table 2 shows the decision making framework for IAVs, it contains five modules and their respective variables related to the articles studied: Trigger point. First at all, the autonomous vehicle has to recognised its current situation and to visualize its goals and priorities. Then the vehicle would look for ways to operate to reach its objectives. In a real world, the objectives do not come alone, they imply to take into account social, political, economical and government regulations. Depending on the situations, sometimes the vehicles can make decisions by their own and sometimes make group decisions. Then the vehicles would have to communicate to each other and to come to an agreement to define the way to operate collectively. The module trigger point does not have references because of is still under discussion, the moment when the vehicle has to make high level decisions involving free operation and interaction with humans and other vehicles is still under evaluation. Protocols need to be established. However, several advantages of IAVs' making decisions can be mentioned: their decisions are not affected by their state of mind or fatigue, they are the result of the evaluation of multiple options, they can manage more data than humans and predict consequences. All the articles presented give an overview of moments when the IAVs have to make decisions. Preparation. The IAV has to know the kind of decisions it is able and allow to make (level of autonomy). When it is working in a group has to evaluate if can count on its partners (trust). To execute an action all its predefined functions need to be verified (reliability on vehicle functions). The context in which the decision is going to be made needs to be assess, circumstances or elements could be changing in time and space (situational awareness). Adequate forms to facilitate information exchange between IAVs and its partners, getting right information and in time is important (interaction). The IAVs also must show its reaction in a clear way. The activities for which the vehicle has to be ready are also determined. In summary, in the preparation process all the elements that defined the vehicle's role are established. Evaluation. It consist of making a detailed study of every possible solutions that were generated for the problem, that is, looking at its advantages and disadvantages, individually with respect to the decision criteria, and one with respect to the other , assigning them a weighted value. The selection would be the one that adjust the most to the goals and criteria. Since we are talking about vehicles having a probabilistic model to quantitatively value their performance, the vehicles would be able to predict, according to their current state, in what degree they could meet their obligations. As well as, foresee the degree of posible damage that their decision could cause. Hence, it could study the probable consequences of its decisions and actions. The origin of the decision and the form the decision is being made is also measured. Execution. Implement the decision made according to the chosen plan in order to evaluate whether the decision was successful or not. The action(s) should be monitor during operations for future evaluations. Until now, the decisions have been implemented on simulations and more simulations are needed to create statistics and different scenarios for possible situations on real life. Before execution; government, society and Autonomous Vehicles companies should come into an agreement to set up the manner IAVs will operate, this includes under what context they will performed (infrastructure conditions and participants). People should be informed about the vehicles skills and behaviour to know what to expect. Results evaluation. Once the decision was executed, it is necessary to evaluate if the problem was solved or not, that is, if the decision is having the expected result or not. In addition, we must be aware that the decisions that are taken continuously will have to be modified, due to the evolution of the situation or the appearance of new variables that affect it. All the modules of the decision making framework not necessarily have to be included in all the situations to make decisions. Maybe for the design and behaviour analysis of the IAV we could need all of them, but for performance in real time we have to select just some of them. The studies exposed above take us to see that the Bayesian networks can let us define clear relationships between factors being part of the decision making framework, we can notice how they influence to each other, where the information and decision are coming from. Therefore, the causes and consequences of the decision can be evaluated. The interaction with other computational models is also possible, they can let us reason with information originated from other computational models or can share information to foresee events. All the decision making modules have been study separately. We could think to have in the future a BN that integrates all the different modules. Consequently, we could count with a computational formalism to study how each module is affecting the decision making. The model could track what module of the decision making framework should be modified to improve the decision or tell us what module took the vehicle to a bad decision. [ BIB002 , [Thieme and Utne, 2017] Trust How much the vehicles believe in the other part with which they are interacting according to the circumstances and their abilities. [Thieme and BIB001 Reliability on vehicle functions The system's ability to perform its functions as required during the time of used. [ BIB002 , [Thieme and Utne, 2017] Situational awareness Perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future. We can have contexts of certainty, risk or uncertainty. [ BIB001 , , [Thieme and Utne, 2017] 