Searched for: subject%3A%22Autonomous%255C+driving%22
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Enting, Marnix (author)
Neural Radiance Fields (NeRFs) have showcased remarkable effectiveness in capturing complex 3D scenes and synthesizing novel viewpoints. By inherently capturing the entire scene in a compact representation, they offer a promising avenue for applications such as simulators, where efficient storage of real-world data, fast rendering and dynamic...
master thesis 2024
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Yin, Lanke (author)
This work introduces a novel training strategy for Gaussian Process (GP) models aimed at improving their predictive accuracy and uncertainty quantification capabilities over extended prediction horizons. This improvement is highly relevant for applications in model predictive control (MPC) in the autonomous driving domain. Learning-based MPC...
master thesis 2024
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CHEN, XI (author)
In autonomous driving, environmental perception, crucial for navigation and decision-making, depends on integrating data from multiple sensors like cameras and LiDAR. Camera-LiDAR fusion combines detailed imagery with precise depth, improving environmental awareness. Effective data fusion requires accurate extrinsic calibration to align camera...
master thesis 2024
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Bērmans, Boriss (author)
Detecting nearby vehicles involves utilizing data from various sensors installed on a car as it moves. Common sensors for identifying nearby vehicles include LiDAR, cameras, and RADAR. However, all of these sensors suffer from the same issue -- they cannot detect an approaching vehicle that is not yet visible. Hence, this thesis explores the...
master thesis 2024
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Damsgaard, Hans Jakob (author), Grenier, Antoine (author), Katare, D. (author), Taufique, Zain (author), Shakibhamedan, Salar (author), Troccoli, Tiago (author), Chatzitsompanis, Georgios (author), Kanduri, Anil (author), Ding, Aaron Yi (author)
Recent advancements in hardware and software systems have been driven by the deployment of emerging smart health and mobility applications. These developments have modernized the traditional approaches by replacing conventional computing systems with cyber–physical and intelligent systems combining the Internet of Things (IoT) with Edge...
review 2024
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Gao, Yuxing (author)
The rapid advancement in autonomous driving technology underscores the importance of studying the fragility of perception systems in autonomous vehicles, particularly due to their profound impact on public transportation safety. These systems are of paramount importance due to their direct impact on the lives of passengers and pedestrians....
master thesis 2023
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van Geerenstein, Mathijs (author)
3D object detection models that exploit both LiDAR and camera sensor features are top performers in large-scale autonomous driving benchmarks. A transformer is a popular network architecture used for this task, in which so-called object queries act as candidate objects. Initializing these object queries based on current sensor inputs leads to...
master thesis 2023
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Spruit, John (author)
With recent advancements in autonomous driving, the demand for precise and accurate perception systems has increased. Perception of the vehicle’s environment is a key element in ensuring safe operation. Due to their wide aperture angle and low cost, ultrasonic sensors are a viable option for achieving close-range 360° perception around the...
master thesis 2023
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Liu, Stan (author)
Learning-based approaches are widely applied in the perception system of autonomous vehicles. Thus, a large amount of labeled data are needed to train these data-hungry models. To reduce the expensive labor cost for manual labeling autonomous driving datasets, an alternative is to automatically annotate the datasets using a trained offline...
master thesis 2023
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LIU, Xinjie (author)
Many autonomous navigation tasks require mobile robots to operate in dynamic environments involving interactions between agents. Developing interaction-aware motion planning algorithms that enable safe and intelligent interactions remains challenging. Dynamic game theory renders a powerful mathematical framework to model these interactions...
master thesis 2023
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Bayram, Ege (author)
Deep reinforcement learning has been a topic of research in recent years and has been expanding into the domain of autonomous driving. As autonomous driving is likely to involve people, such as daily commuters, it is necessary to ensure the machine will perform well enough in real-life environments not to put anyone at risk. There exist possible...
bachelor thesis 2023
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Sozen, Efe (author)
Autonomous driving is a rapidly evolving field that aims to enhance road safety and reduce accidents through the use of advanced software and hardware technologies. Reinforcement learning (RL) combined with deep neural networks has emerged as a promising approach for training autonomous agents. This research paper investigates three exploration...
bachelor thesis 2023
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Seferlis, Ilias (author)
As Autonomous Vehicles (AVs) navigate through dynamic and constantly changing environments, it is crucial that they take into account the impact of their actions on the decisions of others for safe and efficient interaction with humans. In doing so, they need to anticipate how humans will behave in different situations based on their intentions....
master thesis 2023
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Pandharipande, Ashish (author), Cheng, Chih Hong (author), Dauwels, J.H.G. (author), Gurbuz, Sevgi Z. (author), Ibanez-Guzman, Javier (author), Li, Guofa (author), Piazzoni, Andrea (author), Wang, Pu (author), Santra, Avik (author)
Automotive perception involves understanding the external driving environment and the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This article provides an overview of different sensor modalities, such as cameras, radars, and light detection and...
journal article 2023
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DU, YURUI (author)
In recent years, imitation learning (IL) has been widely used in industry as the core of autonomous vehicle (AV) planning modules. However, previous work on IL planners shows sample inefficiency and low generalisation in safety-critical scenarios, on which they are rarely tested. As a result, IL planners can reach a performance plateau where...
master thesis 2022
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Ma, Chenxu (author)
In order to ensure that autonomous driving vehicles can make appropriate driving decisions based on the surrounding situation, motion prediction algorithms are used to generate the driving decision output, which will then be used for guiding the trajectory of the vehicle. In general, the output of the motion prediction algorithm is a series that...
master thesis 2022
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Addi, Rhita (author)
With the introduction of automated driving systems come benefits such as the improvement of traffic safety. However, with an increasing level of automation in vehicles also comes an increase in interaction with in-vehicle technology by drivers while they are meant to supervise the automated driving systems. Due to more interaction with in...
master thesis 2022
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Madi, Mohamed (author)
High level decision making in Autonomous Driving (AD) is a challenging task due to the presence of multiple actors and complex driving interactions. Multi-Agent Reinforcement Learning (MARL) has been proposed to learn multiple driving policies concurrently to solve AD tasks. In the literature, multi-agent algorithms have been shown to outperform...
master thesis 2022
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de Rijk, Philip (author)
Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student. KD has proven to be an effective technique to significantly improve the student's performance for various tasks including object detection. As such, KD techniques mostly rely...
master thesis 2022
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Yang, Fan (author), Li, Xueyuan (author), Liu, Qi (author), Li, Z. (author), Gao, Xin (author)
In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the...
journal article 2022
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