Explanations in autonomous driving: a survey by Omeiza, D, Webb, H, Jirotka, M, Kunze, L

“…This survey aims to provide the fundamental knowledge required of researchers who are interested in explanation provisions in autonomous driving.…”

Uncertainty quantification with statistical guarantees in end-to-end autonomous driving control by Michelmore, R, Wicker, M, Laurenti, L, Cardelli, L, Gal, Y, Kwiatkowska, M

“…Deep neural network controllers for autonomous driving have recently benefited from significant performance improvements, and have begun deployment in the real world. …”

Responsible Innovation; responsible data. A case study in autonomous driving by Ten Holter, C, Kunze, L, Pattinson, J-A, Salvini, P, Jirotka, M

Towards safe and personalized autonomous driving: decision-making and motion control with DPF and CDT techniques by Huang, C, Lv, C, Hang, P, Xing, Y

Can Priors Be Trusted? Learning to Anticipate Roadworks by Mathibela, B, Osborne, M, Posner, I, Newman, P, IEEE

“…This paper addresses the question of how much a previously obtained map of a road environment should be trusted for vehicle localisation during autonomous driving by assessing the probability that roadworks are being traversed. …”

Hybrid photonic integrated circuits for neuromorphic computing [Invited] by Xu, R, Taheriniya, S, Ovvyan, AP, Bankwitz, JR, McRae, L, Jung, E, Brückerhoff-Plückelmann, F, Bente, I, Lenzini, F, Bhaskaran, H, Pernice, WHP

“…Artificial intelligence-related applications, such as autonomous driving, medical diagnosis, and speech recognition, have experienced remarkable progress in recent years; however, such systems require vast amounts of data for accurate inference and reliable performance, presenting challenges in both speed and power consumption. …”

AutoGraph: predicting lane graphs from traffic observations by Zurn, J, Posner, I, Burgard, W

“…Lane graph estimation is a long-standing problem in the context of autonomous driving. Previous works aimed at solving this problem by relying on large-scale, hand-Annotated lane graphs, introducing a data bottleneck for training models to solve this task. …”

VidLoc: A deep spatio-temporal model for 6-DoF video-clip relocalization by Clark, R, Wang, S, Markham, A, Trigoni, N, Wen, H

“…We evaluate our method on openly-available real-world autonomous driving and indoor localization datasets.…”

1 year, 1000 km: the Oxford RobotCar dataset by Maddern, W, Pascoe, G, Linegar, C, Newman, P

“…We present a challenging new dataset for autonomous driving: the Oxford RobotCar Dataset. Over the period of May 2014 to December 2015 we traversed a route through central Oxford twice a week on average using the Oxford RobotCar platform, an autonomous Nissan LEAF. …”

What Could Move? Finding Cars, Pedestrians and Bicyclists in 3D Laser Data by Wang, D, Posner, I, Newman, P, IEEE

“…In particular, we wish to detect instances of classes of interest in autonomous driving applications - cars, pedestrians and bicyclists - amongst significant background clutter. …”

Mitigating distributional shift in semantic segmentation via uncertainty estimation from unlabeled data by Williams, DSW, De Martini, D, Gadd, M, Newman, P

“…To this end, a novel segmentation benchmark based on the sense-assess-eXplain (SAX) is used, which includes labeled test data spanning three autonomous-driving domains, ranging in appearance from dense urban to off-road. …”

Road work ahead: the emerging revolution in the road construction industry by Stern, S, Flyvbjerg, B, Kirchherr, J, Budzier, A, Valtuena-Ramos, G, Agard, K, Reitz, F

“…It is already becoming apparent that four megatrends – autonomous driving, automated production, digitization, and advances in road construction materials – as well as a new process flow for road construction are bound to not only make the roads of the future look significantly different from those of today, but also make road construction much faster and cheaper.…”

Chalcogenide phase-change devices for neuromorphic photonic computing by Brueckerhoff-Plueckelmann, F, Feldmann, J, Wright, CD, Bhaskaran, H, Pernice, WHP

“…The integration of artificial intelligence systems into daily applications like speech recognition and autonomous driving rapidly increases the amount of data generated and processed. …”

Parallel convolutional processing using an integrated photonic tensor core by Feldmann, J, Youngblood, N, Karpov, M, Gehring, H, Li, X, Stappers, M, Le Gallo, M, Fu, X, Lukashchuk, A, Raja, AS, Liu, J, Wright, CD, Sebastian, A, Kippenberg, TJ, Pernice, WHP, Bhaskaran, H

“…Although we focus on convolutional processing, more generally our results indicate the potential of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services.…”

Genomic code for Sox10 activation reveals a key regulatory enhancer for cranial neural crest. by Betancur, P, Bronner-Fraser, M, Sauka-Spengler, T

“…Analyses of the gene regulatory network driving neural crest development revealed Sox10 as one of the earliest neural crest-specifying genes, cell-autonomously driving delamination and directly regulating numerous downstream effectors and differentiation gene batteries. …”