A Study of Artificial Intelligence in the Production of Film by Sun Peiming

“…In recent years, AI has been rapidly developed and widely deployed in a variety of applications in different industries such as autonomous driving, healthcare, financial services, and film production. …”
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Comparative Evaluation of a Trajectory Generator for Obstacle Avoidance Guaranteeing Computational Upper Cost by Isao Okawa, Kenichiro Nonaka

“…Since the proposed method generates quasi-optimal trajectories using model predictive control (MPC) theory with a predetermined upper bound on computational cost, it makes it easy to guarantee real-time feasibility for autonomous driving and/or driving support systems. Numerical round-robin simulations are conducted for both the proposed method and a comparative method, after which we evaluate the results through statistical analysis and individually analyze several characteristic results. …”
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Panoptic image segmentation by Chua, Shahrin Zong Da

“…As a result, the application of Machine Learning to carry out everyday tasks has become increasingly commonplace, such as in autonomous driving. This project focuses on the Panoptic Image Segmentation task, an image segmentation method used for Computer Vision tasks, and aims to implement a real-time danger detection system. …”
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Image processing based lane and kerb detection by Tan, Kuan Hong

“…The data can then be used to provide lane departure warning and avoidance for UGVs and also to augment other sensors such as radar, sonar and LIDAR for fully autonomous driving.…”
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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. …”

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. …”

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. …”

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)…”

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. …”

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. …”

Large-Scale Outdoor SLAM Based on 2D Lidar by Ruike Ren, Hao Fu, Meiping Wu

“…For autonomous driving, it is important to navigate in an unknown environment. …”
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A New Self-Tuning Nonlinear Model Predictive Controller for Autonomous Vehicles by Yasin Abdolahi, Sajad Yousefi, Jafar Tavoosi

“…Autonomous driving has recently been in considerable progress, and many algorithms have been suggested to control the motions of driverless cars. …”
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Technology Integration and Analysis Using Boosting and Ensemble by Sunghae Jun

“…Most of the studies related to technology analysis have focused on one specific technological field such as autonomous driving or blockchain. Most technologies have large and small relationships with each other. …”
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Object Recognition and Tracking in Moving Videos for Maritime Autonomous Surface Ships by Hyunjin Park, Seung-Ho Ham, Taekyeong Kim, Donghyeok An

“…In autonomous driving technologies, a camera is necessary for establishing a path and detecting an object. …”
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Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck by Xiaowei Lu, Yunfeng Ai, Bin Tian

“…Road boundary detection is an important part of the perception of the autonomous driving. It is difficult to detect road boundaries of unstructured roads because there are no curbs. …”
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LMFRNet: A Lightweight Convolutional Neural Network Model for Image Analysis by Guangquan Wan, Lan Yao

“…With their widespread adoption in fields like medical diagnosis and autonomous driving, CNNs have demonstrated powerful capabilities. …”
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Lightweight Convolutional Neural Networks with Model-Switching Architecture for Multi-Scenario Road Semantic Segmentation by Peng-Wei Lin, Chih-Ming Hsu

“…A convolutional neural network (CNN) that was trained using datasets for multiple scenarios was proposed to facilitate real-time road semantic segmentation for various scenarios encountered in autonomous driving. However, the CNN inhibited the mutual suppression effect between weights; thus, it did not perform as well as a network that was trained using a single scenario. …”
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