AI

This is a category for all research topics / papers done within A2I.
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Auditory perception

Abstract - Urban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be highly beneficial for smart vehicle systems, as it would provide them with complementary information to navigate safely in the environment. In this paper, we [...]

Auditory perception2017-09-18T22:42:03+01:00

Probabilistic Prediction of Perception Performance

Learn from Experience: Probabilistic Prediction of Perception Performance to Avoid Failure Abstract –Despite the significant advances in machine learning and perception over the past few decades, perception algorithms can still be unreliable when deployed in challenging, time-varying environments. When these systems are used for autonomous decision-making, such as in self-driving vehicles, the impact of their [...]

Probabilistic Prediction of Perception Performance2017-09-18T22:42:07+01:00

Efficient Object Detection from 3D Point Clouds

Learning Sparse Representations with CNNs for Efficient Object Detection in 3D Point Clouds Abstract – Convolutional neural networks (CNNs) have exhibited state-of-the-art performance across a number of domains, but have yet to realise the same success when applied to 3D point cloud data. This is in part due to the third spatial dimension, which renders the [...]

Efficient Object Detection from 3D Point Clouds2017-09-18T22:41:32+01:00

Introspection

In the context of decision making in robotics, the use of a classification framework which produces scores with inappropriate confidences will ultimately lead to the robot making dangerous decisions. In order to select a framework which will make the best decisions, we should pay careful attention to the ways in which it generates scores. Precision [...]

Introspection2017-09-14T13:42:23+01:00

Knowing When We Don’t Know: Introspective Classification for Mission-Critical Decision Making

Classification precision and recall have been widely adopted by roboticists as canonical metrics to quantify the performance of learning algorithms. This paper advocates that for robotics applications, which often involve mission-critical decision making, good performance according to these standard metrics is desirable but insufficient to appropriately characterise system performance. We introduce and motivate the importance [...]

Knowing When We Don’t Know: Introspective Classification for Mission-Critical Decision Making2017-09-14T13:11:13+01:00