Autonomous underwater vehicles (AUVs) are widely used to perform information gathering missions in unseen environments. Given the sheer size of the ocean environment, and the time and energy constraints of an AUV, it is important to consider the potential utility of candidate missions when performing survey planning. In this paper, we utilise a multimodal learning approach to capture the relationship between in-situ visual observations, and shipborne bathymetry (ocean depth) data that are freely available a priori. We then derive information-theoretic measures under this model that predict the amount of visual information gain at an unobserved location based on the bathymetric features. Unlike previous approaches, these measures consider the value of additional visual features, rather than just the habitat labels obtained. Experimental results with a toy dataset and real marine data demonstrate that the approach can be used to predict the true utility of unexplored areas.

Innovation in intelligent transportation systems relies on analysis of high-quality data. In this paper, we describe the design principles behind our data management infrastructure. The principles we adopt place an emphasis on flexibility and maintainability. This is achieved by breaking up code into a modular design that can be run on many independent processes. Message passing over a publish-subscribe network enables interprocess communication and promotes data-driven execution. By following these principles, rapid prototyping and experimentation with new sensing modalities and algorithms are possible. The communication library underpinning our proposed architecture is compared against several popular communication libraries. Features designed into the system make it decentralized, robust to failure, and amenable to scaling across multiple machines with minimal configuration. Code written using the proposed architecture is compact, transparent, and easy to maintain. Experimentation shows that our proposed architecture offers a high performance when compared against alternative communication libraries.

Intelligent transportation systems are able to collect large volumes of high-resolution data. The amount of data collected by these systems can quickly overwhelm the ability of human analysts to draw meaningful conclusions from the data, particularly in large-scale multivehicle field trials. As advanced driver assistance systems develop, they will also be required to form a rich and high-level understanding of the world from the data they receive, including the behavior of the driver. These applications motivate the need for unsupervised tools capable of forming a high-level summary of low-level driving data. This paper presents an unsupervised method for converting naturalistic driving data into high-level behaviors. The proposed method works in two steps. In the first step, inertial data are automatically decomposed into linear segments. In the second step, the segments are assigned to high-level driving behaviors. The proposed method is computationally efficient and completely unsupervised and requires minimal preprocessing. Although the method is unsupervised, the clusters produced exhibit high-level patterns that can easily be associated with driving behaviors such as braking, turning, accelerating, and coasting. The effectiveness of the proposed algorithms is demonstrated in an offline application where the objective is to summarize inertial data into driving behaviors. The method is also demonstrated in an online application where the aim is to infer the current driving behavior using only inertial data. Both experiments were conducted using driving data collected in natural driving conditions.

Maturing technology has allowed the reliable deployment of robots into large-scale environments for monitoring and exploration applications. Planning techniques which ignore the value of information gathered during transit are able to operate efficiently in these environments and generate trajectories between specified starting and ending locations. Including the value of information gathered during transit increases the complexity of the problem and often leads to algorithms which are unable to scale up to large environments. This paper presents a method for planning informative surveys in large-scale unexplored environments. The proposed methodology does not require a starting or ending location as a constraint. Instead, robot operators are required to specify a survey template, which satisfies both vehicle constraints and the scientific objectives of the deployment. This constraint converts the exploration problem into an experimental design problem where the objective is to choose a location for the specified survey trajectory. A functional representation of the survey utility is learnt using a Gaussian process. This model allows the utility of candidate survey placements to be queried in a continuous space and in arbitrary locations. The proposed exploration method is demonstrated and validated on marine data. The objective is to design a survey which allows the spatial distribution of habitats in a large marine environment to be estimated accurately. The results show that the proposed exploration method is able to model the hidden survey utility function successfully and recommend informative survey placements.

Modern robotic platforms, deployed for environmental monitoring and mapping, are able to rapidly accumulate large data sets. Whilst the data sets collected by these platforms are highly descriptive, they are often too large for human experts to analyse exhaustively. Although the large data sets could be analysed by humans in principle, the amount of labour and time required to process them is not cost effective. In this paper we focus on the classification task of learning the relationship between low resolution, remotely sensed data and categories derived from direct observations of the same phenomenon. To reduce the labour requirements of categorising the direct observations we forgo human supervision and rely on an unsupervised clustering model to segregate the observations into similar groups of data. Rather than using the discrete cluster labels to train a conventional classifier, we develop a new Gaussian process classifier capable of accepting probabilistic training targets. This allows the probabilistic information generated during clustering to be preserved during classification. We demonstrate the new model, in an environmental monitoring application, using data collected by an autonomous underwater vehicle.