Browsing Electrical and Computer Engineering by Supervisor "Czarnecki, Krzysztof"
Now showing items 1-18 of 18
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Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach
(University of Waterloo, 2019-05-23)Autonomous driving is a challenging domain that entails multiple aspects: a vehicle should be able to drive to its destination as fast as possible while avoiding collision, obeying traffic rules and ensuring the comfort ... -
Autonomous Driving: Mapping and Behavior Planning for Crosswalks
(University of Waterloo, 2019-09-23)As autonomous driving integrates with every day traffic, early adopters are initially skeptical and designers are overly cautious. With safety as the top priority, current systems are sometimes too slow at executing maneuvers. ... -
Bayesian Deep Learning and Uncertainty in Computer Vision
(University of Waterloo, 2019-09-17)Visual data contains rich information about the operating environment of an intelligent robotic system. Extracting this information allows intelligent systems to reason and decide their future actions. Erroneous visual ... -
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection
(University of Waterloo, 2022-02-09)The estimation of uncertainty in robotic vision, such as 3D object detection, is an essential component in developing safe autonomous systems aware of their own performance. However, the deployment of current uncertainty ... -
Machine Learning for SAT Solvers
(University of Waterloo, 2018-12-07)Boolean SAT solvers are indispensable tools in a variety of domains in computer science and engineering where efficient search is required. Not only does this relieve the burden on the users of implementing their own search ... -
Managing Consistency of Business Process Models across Abstraction Levels
(University of Waterloo, 2014-04-08)Process models support the transition from business requirements to IT implementations. An organization that adopts process modeling often maintain several co-existing models of the same business process. These models ... -
Motion Planning and Safety for Autonomous Driving
(University of Waterloo, 2019-12-11)This thesis discusses two different problems in motion planning for autonomous driving. The first is the problem of optimizing a lattice planner control set for any particular autonomous driving task, with the goal of ... -
Out-of-Distribution Detection for LiDAR-based 3D Object Detection
(University of Waterloo, 2022-01-18)3D object detection is an essential part of automated driving, and deep neural networks (DNNs) have achieved state-of-the-art performance for this task. However, deep models are notorious for assigning high confidence ... -
Perception and Prediction in Multi-Agent Urban Traffic Scenarios for Autonomous Driving
(University of Waterloo, 2023-09-21)In multi-agent urban scenarios, autonomous vehicles navigate an intricate network of interactions with a variety of agents, necessitating advanced perception modeling and trajectory prediction. Research to improve perception ... -
Runtime Restriction of the Operational Design Domain: A Safety Concept for Automated Vehicles
(University of Waterloo, 2018-06-14)Automated vehicles need to operate safely in a wide range of environments and hazards. The complex systems that make up an automated vehicle must also ensure safety in the event of system failures. This thesis proposes an ... -
Scenario Modeling and Execution for Simulation Testing of Automated-Driving Systems
(University of Waterloo, 2022-09-28)Automated Driving Systems (ADS) have the potential to significantly impact the future of ground mobility. However, safety assurance is still a major obstacle. Field testing alone is impractical and simulation is required ... -
Sparse2SOAP: Domain Adaptation for LiDAR-Based 3D Object Detection
(University of Waterloo, 2023-05-25)In this work, we propose Sparse2SOAP, an extension of the previous work in Sparse2Dense that uses knowledge distillation in a teacher-student framework to densify 3D features, to enable its uses for cross-domain LiDAR-based ... -
Synthesis and Exploration of Multi-Level, Multi-Perspective Architectures of Automotive Embedded System
(University of Waterloo, 2016-08-16)In industry, evaluating candidate architectures of automotive embedded systems is routinely done during the design process. Today's engineers, however, are limited in the number of candidates that they are able to evaluate ... -
Towards a Better Understanding of Variability Evolution
(University of Waterloo, 2016-06-07)Highly-configurable software systems often leverage variability modeling to achieve systematical reuse and mass customization. Although facilitating variability management, variability models do not eliminate the variability ... -
Towards Synthetic Dataset Generation for Semantic Segmentation Networks
(University of Waterloo, 2019-09-23)Recent work in semantic segmentation research for autonomous vehicles has shifted towards multimodal techniques. The driving factor behind this is a lack of reliable and ample ground truth annotation data of real-world ... -
A User-Centric Approach to Improve the Quality of UML-like Modelling Tools and Reduce the Efforts of Modelling
(University of Waterloo, 2020-01-28)As software systems grow in size and complexity, their development and maintenance are becoming increasingly challenging. Model-Driven Engineering (MDE) has been proposed as a means to increase the developer's productivity ... -
Vision Augmented State Estimation with Fault Tolerance
(University of Waterloo, 2018-05-15)Obtaining accurate and reliable measurement data is really crucial for any vehicle system, especially if the system deals with maintaining safe operations of the vehicle. Conventional direct methods of obtaining such ... -
XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection
(University of Waterloo, 2022-01-18)Explainable AI (XAI) methods are frequently applied to obtain qualitative insights about deep models' predictions. However, such insights need to be interpreted by a human observer to be useful. In this thesis, we aim to ...