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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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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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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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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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Tang, Ruifan (author), De Donato, Lorenzo (author), Bešinović, Nikola (author), Flammini, Francesco (author), Goverde, R.M.P. (author), Lin, Zhiyuan (author), Liu, Ronghui (author), Tang, Tianli (author), Vittorini, Valeria (author), Wang, Z. (author)Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective,...review 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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Nurunnabi, A. (author), Teferle, F. N. (author), Lindenbergh, R.C. (author), Li, J. (author), Zlatanova, S. (author)Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road scanning opportunity. Many methods are available for road pavement, curb and roadside way extraction. Most of them use classical approaches...journal article 2022
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Perez Dattari, R.J. (author), Ferreira de Brito, B.F. (author), de Groot, O.M. (author), Kober, J. (author), Alonso Mora, J. (author)The successful integration of autonomous robots in real-world environments strongly depends on their ability to reason from context and take socially acceptable actions. Current autonomous navigation systems mainly rely on geometric information and hard-coded rules to induce safe and socially compliant behaviors. Yet, in unstructured urban...journal article 2022
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ZHANG, Bichi (author)In recent decades, the field of autonomous driving has witnessed rapid development, benefiting from the development of artificial intelligence-related technologies such as machine learning. Autonomous perception in driving is a key challenge, in which multi-sensor fusion is a common feature. Due to the high resolution and rich information, the...master thesis 2021
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Schulz, Yannick (author)Driving is a challenging task. When people operate vehicles they utilize all their senses to assess the current traffic scenario and determine appropriate actions to take. Sensors in autonomous driving applications aim to mimic those human senses to build a similar understanding of these complex circumstances. Most scientific attention in the...master thesis 2021
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Razdan, Shuhul (author)With inland transportation increasing every passing day, vehicle platooning offers a good solution towards travelling more efficiently. Along with reducing traffic congestion on roads, platooning also leads to better fuel consumption among vehicles, fewer accidents, and most importantly, vehicle platoons can be made autonomous using optimization...master thesis 2021