The Internet of Things (IoT) is set to become one of the key technological developments of our times provided we are able to realize its full potential. The number of objects connected to IoT is expected to reach 50 billion by 2020 due to the massive influx of diverse objects emerging progressively. IoT, hence, is expected to be a major producer of big data. Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous environments, such as smart cities and societies. A timely fusion and analysis of big data, acquired from IoT and other sources, to enable highly efficient, reliable, and accurate decision making and management of ubiquitous environments would be a grand future challenge. Computational intelligence would play a key role in this challenge. A number of surveys exist on data fusion. However, these are mainly focused on specific application areas or classifications. The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments). The opportunities and challenges for each of the mathematical methods and environments are given. Future developments, including emerging areas that would intrinsically benefit from data fusion and IoT, autonomous vehicles, deep learning for data fusion, and smart cities, are discussed.

Data Fusion and IoT for Smart Ubiquitous Environments: A Survey

A vehicle awareness system for monitoring remote vehicles relative to a host vehicle. The vehicle awareness system includes at least one object sensing device and a vehicle-to-vehicle communication device. A data collection module is provided for obtaining a sensor object data map and vehicle-to-vehicle object data map. A fusion module merges the sensor object data map and vehicle-to-vehicle object data map for generating a cumulative object data map. A tracking module estimates the relative position of the remote vehicles to the host vehicle.

Combined Vehicle-to-Vehicle Communication and Object Detection Sensing

A method of estimating a time to collision (TTC) of a vehicle with an object comprising: acquiring a plurality of images of the object; and determining a TTC from the images that is responsive to a relative velocity and relative acceleration between the vehicle and the object.

Collision warning system

A processor (26) using a first Kalman filter (52, 52.1) estimates a host vehicle state from speed (U) and yaw rate, the latter of which may be from a yaw rate sensor (16) if speed (U) is greater than a threshold, and, if less, from a steer angle sensor and speed (U). Road curvature parameters (C0, C1) are estimated from a curve fit of a host vehicle trajectory or from a second Kalman filter (54, 54.1) for which a state variable may be responsive to a plurality of host state variables (72, 74). Kalman filters (52, 52.1, 54, 54.1) may incorporate adaptive sliding windows. Curvature of a most likely road type is estimated with an interacting multiple model (IMM) algorithm (2400) using models of different road types. A road curvature fusion subsystem (96) provides for fusing road curvature estimates from a plurality of curvature estimators (42.1, 42.2, 42.N) using either host vehicle state, a map database (88) responsive to vehicle location (86), or measurements of a target vehicle (36) with a radar system (14).

Road curvature estimation system

A method for collision avoidance for a host vehicle includes the following steps; receiving input data relating to a set of objects external to the host vehicle, wherein an object position (r,Φ), and an object velocity ({dot over (r)}) are associated with each object by a sensor system arranged on the host vehicle, then estimating future trajectories of each external object, while considering influence by the future trajectories of the other external objects.

Method and system for collision avoidance

A first Kalman filter estimates true measures of yaw rate and vehicle speed from associated noisy measures thereof generated by respective sensors in a host vehicle, and a second Kalman filter estimates therefrom parameters of a clothoid model of road curvature. Measures of range, range rate, and azimuth angle from a target state estimation subsystem, e.g. a radar system, are processed by an extended Kalman filter to provide an unconstrained estimate of the state of a target vehicle. Associated road constrained target state estimates are generated for one or more roadway lanes, and are compared—either individually or in combination—with the unconstrained estimate. If a constrained target state estimate corresponds to the unconstrained estimate, then the state of the target vehicle is generated by fusing the unconstrained and constrained estimates; and otherwise is given by the unconstrained estimate alone.

Road curvature estimation and automotive target state estimation system

This paper presents a detailed analysis of the computational complexity of multiple hypothesis tracking (MHT). The result shows that the computational complexity of MHT is dominated by the number of hypotheses. Track merging and pruning are also analyzed. The results of this paper provide a new efficient tool for selecting parameters for an MHT tracker and predicting its complexity.

Computational complexity analysis for multiple hypothesis tracking

Interval models for target tracking algorithms

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Road curvature estimation system

Road curvature estimation and automotive target state estimation system

Computational complexity analysis for multiple hypothesis tracking

Interval models for target tracking algorithms

Computational complexity analysis for multiple hypothesis tracking