Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles

The advancements in connected and autonomous vehicles in these times demand the availability of tools providing the agents with the capability to be aware and predict their own states and context dynamics. This article presents a novel approach to develop an initial level of collective awareness (CA) in a network of intelligent agents. A specific collective self-awareness functionality is considered, namely, agent-centered detection of abnormal situations present in the environment around any agent in the network. Moreover, the agent should be capable of analyzing how such abnormalities can influence the future actions of each agent. Data-driven dynamic Bayesian network (DBN) models learned from time series of sensory data recorded during the realization of tasks (agent network experiences) are here used for abnormality detection and prediction. A set of DBNs, each related to an agent, is used to allow the agents in the network to reach synchronously aware possible abnormalities occurring when available models are used on a new instance of the task for which DBNs have been learned. A growing neural gas (GNG) algorithm is used to learn the node variables and conditional probabilities linking nodes in the DBN models; a Markov jump particle filter (MJPF) is employed for state estimation and abnormality detection in each agent using learned DBNs as filter parameters. Performance metrics are discussed to asses the algorithm’s reliability and accuracy. The impact is also evaluated by the communication channel used by the network to share the data sensed in a distributed way by each agent of the network. The IEEE 802.11p protocol standard has been considered for communication among agents. Performances of the DBN-based abnormality detection models under different channel and source conditions are discussed. The effects of distances among agents and of the delays and packet losses are analyzed in different scenario categories (urban, suburban, and rural). Real data sets are also used acquired by autonomous vehicles performing different tasks in a controlled environment.

[1]  Bruce Hood,et al.  The Self Illusion: How the Social Brain Creates Identity , 2001 .

[2]  C. Koch,et al.  Neural correlates of consciousness: progress and problems , 2016, Nature Reviews Neuroscience.

[3]  Jouko Lampinen,et al.  Rao-Blackwellized particle filter for multiple target tracking , 2007, Inf. Fusion.

[4]  Michael I. Jordan,et al.  Nonparametric Bayesian Learning of Switching Linear Dynamical Systems , 2008, NIPS.

[5]  Karl J. Friston,et al.  Predictive coding under the free-energy principle , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[6]  Jörg Ott,et al.  The ONE simulator for DTN protocol evaluation , 2009, SimuTools.

[7]  W. Tian,et al.  Particle filter for sensor fusion in a land vehicle navigation system , 2005 .

[8]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[9]  Seema Bawa,et al.  A systematic review on routing protocols for Vehicular Ad Hoc Networks , 2014, Veh. Commun..

[10]  Jörg Ott,et al.  The ONE simulator for DTN protocol evaluation , 2009, SIMUTools 2009.

[11]  H. Critchley,et al.  An Interoceptive Predictive Coding Model of Conscious Presence , 2011, Front. Psychology.

[12]  Ch. Ramesh Babu,et al.  Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds , 2016 .

[13]  Karl J. Friston,et al.  Bayesian state estimation using generalized coordinates , 2011, Defense + Commercial Sensing.

[14]  Mario Marchese,et al.  Dynamic Bayesian Approach for decision-making in Ego-Things , 2019, 2019 IEEE 5th World Forum on Internet of Things (WF-IoT).

[15]  Manos Tsakiris,et al.  The Experience of Agency , 2009 .

[16]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[17]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[18]  Adrian Perrig,et al.  Challenges in Securing Vehicular Networks , 2005 .

[19]  A. Agresti.  [A Survey of Exact Inference for Contingency Tables]: Rejoinder , 1992 .

[20]  Antonio Chella,et al.  Machine consciousness: A manifesto for robotics , 2009 .

[21]  Neil Salkind.  Encyclopedia of Measurement and Statistics , 2006 .

[22]  Sergio Verdú,et al.  Total variation distance and the distribution of relative information , 2014, 2014 Information Theory and Applications Workshop (ITA).

[23]  Raed A. Abd-Alhameed,et al.  Probability Distribution of Rician $K$-Factor in Urban, Suburban and Rural Areas Using Real-World Captured Data , 2014, IEEE Transactions on Antennas and Propagation.

[24]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[25]  Carlo S. Regazzoni,et al.  Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving , 2018, 2018 21st International Conference on Information Fusion (FUSION).

[26]  Qihui Wu,et al.  An Amateur Drone Surveillance System Based on the Cognitive Internet of Things , 2017, IEEE Communications Magazine.

[27]  Sherali Zeadally,et al.  Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies , 2015, IEEE Wireless Communications.

[28]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[29]  Carlo S. Regazzoni,et al.  Learning Switching Models for Abnormality Detection for Autonomous Driving , 2018, 2018 21st International Conference on Information Fusion (FUSION).

[30]  S. Dehaene,et al.  Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework , 2001, Cognition.

[31]  R. Beran.  Minimum Hellinger distance estimates for parametric models , 1977 .

[32]  Anna Maria Vegni,et al.  A Survey on Vehicular Social Networks , 2015, IEEE Communications Surveys & Tutorials.

[33]  Daphne Koller,et al.  Sampling in Factored Dynamic Systems , 2001, Sequential Monte Carlo Methods in Practice.

[34]  Arturo de la Escalera,et al.  Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles , 2018 .

[35]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[36]  Barbara M. Masini,et al.  Cooperative Awareness in the Internet of Vehicles for Safety Enhancement , 2017 .

[37]  Seul-Ki Han,et al.  TDOA/FDOA based target tracking with imperfect position and velocity data of distributed moving sensors , 2017 .

[38]  A. Morin.  Levels of consciousness and self-awareness: A comparison and integration of various neurocognitive views , 2006, Consciousness and Cognition.

[39]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[40]  R. P. Marques,et al.  Discrete-Time Markov Jump Linear Systems , 2004, IEEE Transactions on Automatic Control.

[41]  Xuemin Shen,et al.  Connected Vehicles: Solutions and Challenges , 2014, IEEE Internet of Things Journal.

[42]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[43]  Mahdyar Ravanbakhsh,et al.  a Multi-Perspective Approach to Anomaly Detection for Self -Aware Embodied Agents , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[44]  M. Shadlen,et al.  Consciousness as a Decision to Engage , 2011 .

[45]  Alejandro Betancourt,et al.  Static force field representation of environments based on agents’ nonlinear motions , 2017, EURASIP Journal on Advances in Signal Processing.

[46]  Rami Bahsoon,et al.  A Survey of Self-Awareness and Its Application in Computing Systems , 2011, 2011 Fifth IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops.

[47]  Bernd Fritzke,et al.  A Growing Neural Gas Network Learns Topologies , 1994, NIPS.

[48]  Rodolfo Lourenzutti,et al.  The Hellinger distance in Multicriteria Decision Making: An illustration to the TOPSIS and TODIM methods , 2014, Expert Syst. Appl..

[49]  Bernd Fritzke,et al.  A Self-Organizing Network that Can Follow Non-stationary Distributions , 1997, ICANN.

[50]  Eylem Ekici,et al.  Wireless Access in Vehicular Environments , 2009, EURASIP J. Wirel. Commun. Netw..