Electrical and Computer Engineering
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Browsing Electrical and Computer Engineering by Subject "3D object detection"
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Item CADC++: Extending CADC with a Paired Weather Domain Adaptation Dataset for 3D Object Detection in Autonomous Driving(University of Waterloo, 2025-01-28)Lidar sensors enable precise 3D object detection for autonomous driving under clear weather but face significant challenges in snowy conditions due to signal attenuation and backscattering. While prior studies have explored the effects of snowfall on lidar returns, its impact on 3D object detection performance remains underexplored. Conducting such an evaluation objectively requires a dataset with abundant labelled data from both weather conditions and ideally captured in the same driving environment. Current driving datasets with lidar data either do not provide enough labelled data in both snowy and clear weather conditions, or rely on simulation methods to generate data for the weather domain with insufficient data. Simulations, nevertheless, often lack realism, introducing an additional domain shift that impedes accurate evaluations. This thesis presents our work in creating CADC++, a paired weather domain adaptation dataset that extends the existing snowy dataset, CADC, with clear weather data. Our CADC++ clear weather data have been recorded on the same roads and around the same days as CADC. We pair each CADC sequence with a clear weather one as closely as possible, both spatially and temporally. Our curated CADC++ achieves similar object distributions as CADC, enabling minimal domain shift in environmental factors beyond the presence of snow. Additionally, we propose track-based auto-labelling methods to overcome a limited labelling budget. Our approach, evaluated on the Waymo Open Dataset, achieves a balanced performance across stationary and dynamic objects and still surpasses a standard 3D object detector when using as low as 0.5% of human-annotated ground-truth labels.Item 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 modeling and trajectory prediction in autonomous vehicles is fundamental to enhance safety and efficiency in complex driving scenarios. Better data association for 3D multi-object tracking ensures consistent identification and tracking of multiple objects over time, crucial in crowded urban environments to avoid mis-identifications that can lead to unsafe maneuvers or collisions. Effective context modeling for 3D object detection aids in interpreting complex scenes, effectively dealing with challenges like noisy or missing points in sensor data, and occlusions. It enables the system to infer properties of partially observed or obscured objects, enhancing the robustness of the autonomous system in varying conditions. Furthermore, improved trajectory prediction of surrounding vehicles allows an autonomous vehicle to anticipate future actions of other road agents and adapt accordingly, crucial in scenarios like merging lanes, making unprotected turns, or navigating intersections. In essence, these research directions are key to mitigating risks in autonomous driving, and facilitating seamless interaction with other road users. In Part I, we address the task of improving perception modeling for AV systems. Concretely our contributions are: (i) FANTrack introduces a novel application of Convolutional Neural Networks (CNNs) for real-time 3D Multi-object Tracking (MOT) in autonomous driving, addressing challenges such as varying number of targets, track fragmentation, and noisy detections, thereby enhancing the accuracy of perception capabilities for safe and efficient navigation. (ii) FANTrack proposes to leverage both visual and 3D bounding box data, utilizing Siamese networks and hard-mining, to enhance the similarity functions used in data associations for 3D Multi-object Tracking (MOT). (iii) SA-Det3D introduces a globally-adaptive Full Self-Attention (FSA) module for enhanced feature extraction in 3D object detection, overcoming the limitations of traditional convolution-based techniques by facilitating adaptive context aggregation from entire point-cloud data, thereby bolstering perception modeling in autonomous driving. (iv) SA-Det3D also introduces the Deformable Self-Attention (DSA) module, a scalable adaptation for global context assimilation in large-scale point-cloud datasets, designed to select and focus on most informative regions, thereby improving the quality of feature descriptors and perception modeling in autonomous driving. In Part II, we focus on the task of improving trajectory prediction of surrounding agents. Concretely, our contributions are: (i) SSL-Lanes introduces a self-supervised learning approach for motion forecasting in autonomous driving that enhances accuracy and generalizability without compromising inference speed or model simplicity, utilizing pseudo-labels from pretext tasks for learning transferable motion patterns. (ii) The second contribution in SSL-Lanes is the design of comprehensive experiments to demonstrate that SSL-Lanes can yield more generalizable and robust trajectory predictions than traditional supervised learning approaches. (iii) SSL-Interactions presents a new framework that utilizes pretext tasks to enhance interaction modeling for trajectory prediction in autonomous driving. (iv) SSL-Interactions advances the prediction of agent trajectories in interaction-centric scenarios by creating a curated dataset that explicitly labels meaningful interactions, thus enabling the effective training of a predictor utilizing pretext tasks and enhancing the modeling of agent-agent interactions in autonomous driving environments.