Pedestrian and ego-vehicle trajectory prediction from monocular camera by Neumann, L, Vedaldi, A

“…Predicting future pedestrian trajectory is a crucial component of autonomous driving systems, as recognizing critical situations based only on current pedestrian position may come too late for any meaningful corrective action (e.g. breaking) to take place. …”

Relaxed softmax: efficient confidence auto-calibration for safe pedestrian detection by Neumann, L, Zisserman, A, Vedaldi, A

“…The clearest example are safety-critical applications such as pedestrian detection in autonomous driving. Since algorithms can never be expected to be perfect in all cases, managing reliability becomes crucial. …”

Real time monocular vehicle velocity estimation using synthetic data by McCraith, R, Neumann, L, Vedaldi, A

“…Vision is one of the primary sensing modalities in autonomous driving. In this paper we look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. …”

Interpretable explanations of black boxes by meaningful perturbation by Fong, RC, Vedaldi, A

“…As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions. …”

Tiny people pose by Neumann, L, Vedaldi, A

“…This is relevant when interpreting people at a distance, which is important in applications such as autonomous driving and surveillance in crowds. Addressing this challenge, which has received little attention so far, can inspire modifications of traditional deep learning approaches that are likely to be applicable well beyond the case of pose recognition. …”

Learning and interpreting deep representations from multi-modal data by Patrick, M

“…<p>Deep learning has resulted in ground-breaking progress in a variety of domains, from core machine learning tasks such as image, language, and video understanding, to real-world industries such as medicine, autonomous driving, and agriculture. Its success has been driven by providing neural networks with manual supervision from large-scale labelled datasets such as ImageNet to automatically learn hierarchical data representations. …”

Understanding convolutional neural networks by Fong, R

“…However, as deep learning is increasingly being applied to high-impact domains, like medical diagnosis or autonomous driving, the impact of its failures also increases. …”