Towards accountability: providing intelligible explanations in autonomous driving by Omeiza, DA, Webb, H, Jirotka, M, Kunze, L

Why not explain? effects of explanations on human perceptions of autonomous driving by Omeiza, D, Kollnig, K, Webb, H, Jirotka, M, Kunze, L

Multi-weather city: adverse weather stacking for autonomous driving by Musat, V, Fursa, I, Newman, P, Cuzzolin, F, Bradley, A

Assessing and explaining collision risk in dynamic environments for autonomous driving safety by Nahata, R, Omeiza, D, Howard, R, Kunze, L

“…Moreover, by explaining risk factors to developers and engineers the overall safety of autonomous driving can be increased in future deployments. …”

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. …”

Explainable action prediction through self-supervision on scene graphs by Kochakarn, P, De Martini, D, Omeiza, D, Kunze, L

“…This work explores scene graphs as a distilled representation of high-level information for autonomous driving, applied to future driver-action prediction. …”

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. …”

Adversarial robustness certification for Bayesian neural networks by Wicker, M, Patane, A, Laurenti, L, Kwiatkowska, M

“…We evaluate the effectiveness of our method on tasks including airborne collision avoidance, medical imaging and autonomous driving, demonstrating that it can compute non-trivial guarantees on medium size images (i.e., over 16 thousand input parameters)…”

What makes a place? Building bespoke place dependent object detectors by Hawke, J, Bewley, A, Posner, H

“…We build bespoke pedestrian detector models for autonomous driving, highlighting the necessary trade off between generalisation and model capacity as we vary the extent of the ‘place’ we fit to. …”

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. …”

Learning to adapt for stereo by Tonioni, A, Rahnama, O, Joy, T, Di Stefano, L, Ajanthan, T, Torr, PHS

“…Even though deep learning based stereo methods are successful, they often fail to generalize to unseen variations in the environment, making them less suitable for practical applications such as autonomous driving. In this work, we introduce a ``learning-to-adapt'' framework that enables deep stereo methods to continuously adapt to new target domains in an unsupervised manner. …”

Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles by Wu, M, Louw, T, Lahijanian, M, Ruan, W, Huang, X, Merat, N, Kwiatkowska, M

“…The method is evaluated on a data set of 124 experiments from 75 drivers collected in a safety-critical semi-autonomous driving scenario. The results illustrate the efficacy of the framework by correctly anticipating the drivers’ intentions about 3 seconds beforehand with over 90% accuracy.…”

Addressing appearance change in outdoor robotics with adversarial domain adaptation by Wulfeier, M, Bewley, A, Posner, H

“…The distilled insights are applied to the problem of free-space segmentation for motion planning in autonomous driving.…”

Evaluating temporal observation-based causal discovery techniques applied to road driver behaviour by Howard, R, Kunze, L

“…In order to better explore the issues facing observational techniques and promote further discussion of these topics we carry out a benchmark across 10 contemporary observational temporal causal discovery methods in the domain of autonomous driving. By evaluating these methods upon causal scenes drawn from real world datasets in addition to those generated synthetically we highlight where improvements need to be made in order to facilitate the application of causal discovery techniques to the aforementioned use-cases. …”

Safe POMDP online planning via shielding by Sheng, S, Parker, D, Feng, L

“…But the resulting policies cannot provide safety guarantees which are imperative for real-world safety-critical tasks (e.g., autonomous driving). In this work, we consider safety requirements represented as almost-sure reach-avoid specifications (i.e., the probability to reach a set of goal states is one and the probability to reach a set of unsafe states is zero). …”

Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy by Barnes, D, Maddern, W, Posner, H

“…We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. …”

SimCS: simulation for domain incremental online continual segmentation by Alfarra, M, Cai, Z, Bibi, A, Ghanem, B, Müller, M

“…ODICS arises in many practical applications. In autonomous driving, this may correspond to the realistic scenario of training a segmentation model over time on a sequence of cities. …”

Mining the automotive industry: A network analysis of corporate positioning and technological trends by Stoehr, N, Braesemann, F, Frommelt, M, Zhou, S

“…We tag web pages concerned with topics like e-mobility & environment or autonomous driving, and investigate their relevance in the network. …”

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. …”