Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles <s> (a) Topics <s> The success of a landed space exploration mission depends largely on the final landing site. Factors influencing site selection include safety, fuel-consumption, and scientific return. This paper addresses the problem of selecting the best available landing site based on these factors in real-time during autonomous spacecraft descent onto a planetary surface. The problem is modeled probabilistically using Bayesian Networks (BNs). BNs provide a means of representing the causal relationships between variables that impact the quality of a landing site. The final landing site is determined via probabilistic reasoning based on terrain safety derived from on-board sensors, available fuel based on spacecraft descent dynamics, and regions of interest defined by mission scientists. <s> BIB001 </s> Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles <s> (a) Topics <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> BIB002 </s> Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles <s> (a) Topics <s> Learning Bayesian networks is known to be an NP-hard problem and that is the reason why the application of a heuristic search has proven advantageous in many domains. This learning approach is computationally efficient and, even though it does not guarantee an optimal result, many previous studies have shown that it obtains very good solutions. Hill climbing algorithms are particularly popular because of their good trade-off between computational demands and the quality of the models learned. In spite of this efficiency, when it comes to dealing with high-dimensional datasets, these algorithms can be improved upon, and this is the goal of this paper. Thus, we present an approach to improve hill climbing algorithms based on dynamically restricting the candidate solutions to be evaluated during the search process. This proposal, dynamic restriction, is new because other studies available in the literature about restricted search in the literature are based on two stages rather than only one as it is presented here. In addition to the aforementioned advantages of hill climbing algorithms, we show that under certain conditions the model they return is a minimal I-map of the joint probability distribution underlying the training data, which is a nice theoretical property with practical implications. In this paper we provided theoretical results that guarantee that, under these same conditions, the proposed algorithms also output a minimal I-map. Furthermore, we experimentally test the proposed algorithms over a set of different domains, some of them quite large (up to 800 variables), in order to study their behavior in practice. <s> BIB003 </s> Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles <s> (a) Topics <s> In this paper the problem of online dynamic UAV path planning is studied for the situation where a manoeuvring pop-up threat exists and information about this threat is incomplete. The problem is addressed by integrating a structure-varying discrete dynamic Bayesian network (SVDDBN) into the model prediction control (MPC) algorithm. The SVDDBN is used to construct online a dynamic threat assessment model by estimating and predicting the states of the manoeuvring pop-up threat. The output of this model is then fed into the MPC algorithm for effective path planning. The results obtained in the performed simulation studies demonstrate the excellent performance of the proposed algorithm. <s> BIB004 </s> Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles <s> (a) Topics <s> The unmanned vehicle shows great potential in national economic. The risk level of unmanned vehicle has direct impact on the development of the unmanned transport industry. The Bayes net for the risk of unmanned vehicle are created. The parameters of net are analyzed, and the quantitative computational method for the evaluation is given. A case study on the typical scene is introduced. The simulation proved the feasibility of the method. <s> BIB005 </s> Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles <s> (a) Topics <s> Autonomous marine systems, such as autonomous ships and autonomous underwater vehicles, gain increased interest in industry and academia. Expected benefits of autonomous marine system in comparison to conventional marine systems are reduced cost, reduced risk to operators, and increased efficiency of such systems. Autonomous underwater vehicles are applied in scientific, commercial, and military applications for surveys and inspections of the sea floor, the water column, marine structures, and objects of interest. Autonomous underwater vehicles are costly vehicles and may carry expensive payloads. Hence, risk models are needed to assess the mission success before a mission and adapt the mission plan if necessary. The operators prepare and interact with autonomous underwater vehicles to carry out a mission successfully. Risk models need to reflect these interactions. This article presents a Bayesian belief network to assess the human–autonomy collaboration performance, as part of a risk model for autonomous underwater vehicle operation. Human–autonomy collaboration represents the joint performance of the human operators in conjunction with an autonomous system to achieve a mission aim. A case study shows that the human–autonomy collaboration can be improved in two ways: (1) through better training and inclusion of experienced operators and (2) through improved reliability of autonomous functions and situation awareness of vehicles. It is believed that the human–autonomy collaboration Bayesian belief network can improve autonomous underwater vehicle design and autonomous underwater vehicle operations by clarifying relationships between technical, human, and organizational factors and their influence on mission risk. The article focuses on autonomous underwater vehicle, but the results should be applicable to other types of autonomous marine systems. <s> BIB006 </s> Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles <s> (a) Topics <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> BIB007
Regardless of the means by which Autonomous Vehicles move, all of them in some moment of their performance need to make decisions. The articles are organized by means of transport. However, it can be noticed that the IAVs have to deal with similar aspects in the decision making process in a real world. Thus, the Bayesian networks have been used to model situations that involve the topics showed in Table 1 . The description explains the BN's specific functions. Looking at the publication year of the articles, it comes out that the topics are very new. Advances in vehicle automation have led to let them operate in the real world, without evaluating their impact first. Now the researchers are trying to model the behaviour of IAVs to integrate them into the peoples lives and the society complying with the established parameters. Selection of the best landing site in the space based on areas of scientific interest (decision). BIB001 Situation assessment in a battle field Fusion of information and analysis of variables to study the opponents' reaction (prediction). [ BIB002 Identify behaviour patterns Detect unexpected behaviours or anomalies for threat assessment (anomaly fusion). [ BIB006 Dynamic threat assessment Estimation and prediction of the states of the pop up threats. [ BIB004 Software health management Monitoring of the functioning of the software and hardware and their interaction during system operation (faults detection). Diagnostic reasoning in order to identify the most likely root causes for the faults. [ BIB004 Cooperation on search missions based on IAVs' capabilities Modelling of unmanned autonomous vehicles' capabilities BIB007 Battle damage assessment Damage assessment of the attack from IAV to enemies' ground airfield runway (consequences' prognosis). [Chen-han and Jian, 2014] Safety IAV malfunctioning is evaluated taking into account transport regulations (risk assessment) BIB005 Landing site selection in the event of an emergency Selection of a safe landing site based on priorities (decision). [ BIB007 Cyber-physical threats detection Diagnosis to determine the origin of a threat (cyber attack or sensors attack). [ BIB003 Level of autonomy Prediction of level of autonomy for IAVs' supervision (foresee the mental workload of the human). [ BIB007 