Security investigation of autonomous driving systems by Han, Xingshuo

“…Autonomous Driving (AD) represents a breakthrough technology with immense potential to make our transportation more intelligent, as many Autonomous Vehicles (AV) are already deployed into real products on public roads. …”
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Human-guided reinforcement learning: methodology and application to autonomous driving by Wu, Jingda

“…The proposed methodology in this thesis can contribute to the development of learning-based autonomous driving techniques and has the potential to be applied in a broader range of contexts.…”
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LiDAR-based 3D object detection and tracking for autonomous driving by Luo, Zhipeng

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Robust and light-weight simultaneous localization and mapping for autonomous vehicles by Wang, Han

“…Thorough experiments are performed on both outdoor autonomous driving and indoor warehouse robot manipulation. …”
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Deep learning-based interaction-aware trajectory prediction for autonomous vehicles by Mo, Xiaoyu

“…Predicting future trajectories of surrounding agents and conducting motion planning based on interaction predictions are of great importance for ensuring the safety and efficiency of autonomous driving in real-world scenarios, especially under critical driving scenarios. …”
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Fault detection method based on high-level feedback and reconstruction for image recognition by Xu, Jin

“…This limitation causes great safety concerns for the deployment of existing deep learning systems for critical applications such as autonomous driving and medical diagnosis. However, it is not practical to include all adversarial or unusual images in training because images are highly variable. …”
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Sequence-to-sequence learning for motion prediction and generation by Wu, Shuang

“…The research field for computational understanding and modelling of human motion has garnered increasing importance in the last decade, with a plethora of applications in sports science, animation, robotics, surveillance and autonomous driving. In this thesis, we engage the sequence-to-sequence learning paradigm to study motion prediction and motion generation. …”
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Interactive decision-making for autonomous vehicles based on a theory of order by Cai, Jiacheng

“…Autonomous driving is challenging, because understanding how human-being makes decisions can be difficult. …”
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Accurate and robust detection and recognition of texts in scene by Xue, Chuhui

“…Automated scene text detection and recognition have attracted increasing interest in computer vision and deep learning communities due to its wide range of applications in neural machine translation, autonomous driving, etc. As compared with preliminary research that focuses on the design of hand-crafted features, modern deep-learning-based techniques have achieved significant improvements on scene text detection and recognition tasks. …”
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Towards robust visual recognition: learning from imperfect data by Hu, Xinting

“…First of all, in many real-world applications, such as medical imaging and autonomous driving, the availability of large quantities of labeled data can be severe due to the scarcity of certain cases. …”
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Shared control navigation system for the assistive wheelchair by Lei, Zhen

“…However, many prospective users, especially those with upper limb disability, find it difficult and unsafe to use a manual or powered wheelchair independently in situations where fine motor control is required, such as making sharp turns or traversing narrow doorways. The autonomous driving wheelchair fails to be their choice either because users dislike the sense of not controlling the wheelchair. …”
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Part-based visual tracking with graphs by Han, Wei

“…It is widely adopted in many applications such as video surveillance, robotics, and autonomous driving. Visual tracking is challenging due to the unconstrained real-world scenarios such as large deformation, occlusion, illumination change and cluttered background. …”
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Object detection with deep neural networks under constrained scenarios by Zhang, Gongjie

“…Object detection forms the basis for many other computer vision tasks and has extensive use cases, such as autonomous driving, surveillance, robotic vision, etc. In the past ten years, object detection has made unprecedented progress with the development of deep neural networks. …”
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Vision-based road lane and curb detection for unmanned ground vehicles in urban scenarios by Zhou, Hui

“…Curb is also an important road feature to assist autonomous driving when lane markings can not be found. …”
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Methods for large-scale image-based localization using structure-from-motion point clouds by Cheng, Wentao

“…Yet, it remains difficult but meaningful to build a system, which (i) robustly handles the prohibitively expensive memory consumption brought by large-scale SfM point clouds, (ii) well resolves the match disambiguation problem, i.e. distinguishing correct matches from wrong ones, which is even more challenging in urban scenes or under binary feature representation and (iii) achieves high localization accuracy so that the system can be safely applied in low false tolerance applications such as autonomous driving. In this thesis, we propose three methods that tackle these challenging problems to make a further step towards such an ultimate system. …”
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Context-aware pedestrian motion prediction by Haddad, Sirin

“…Pedestrian motion prediction can enhance the effectiveness of Advanced Driver-Assistance Systems (ADAS), autonomous driving, and robotic navigation to maintain pedestrians safety. …”
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Dense prediction and deep learning in complex visual scenes by Wang, Yi

“…Many computer vision applications, such as video surveillance, autonomous driving, and crowd analysis, suffer from the challenging conditions of complex scenes, including haze, underwater, extreme lighting, and crowded and small objects. …”
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SoCFaSe : in quest for fast and secure SoC architectures by Gupta, Naina

“…Even though this fusion of physical and digital universe has proved to be beneficial in multiple sectors such as healthcare, autonomous driving, smart home, etc. Such a growth has also led to many security challenges especially with the rise in side-channel attack possibilities. …”
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Depth perception in challenging weathers by Zhang, Haoyuan

“…Depth information is a basis for numerous applications in robotics and autonomous driving. With the commonly considered sensors (i.e. color camera and 3D Lidar) in robotics, the mainstream approaches to acquiring dense depth maps are depth estimation based on a monocular camera, stereo matching based on dual cameras, and depth completion based on a single camera and sparse Lidar. …”
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Motion estimation and prediction from 3D point clouds by Li, Ruibo

“…Understanding the motion of dynamic environments holds significant benefits for various applications, including robotics and autonomous driving. Scene flow estimation from 3D point clouds, which outputs a per-point 3D motion field between two consecutive time steps, has garnered increasing attention. …”
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