Evolvement of Scheduling Theories for Autonomous Vehicles

Chang, Wanli; Chen, Nan; Zhao, Shuai; Dai, Xiaotian

doi:10.1007/978-3-031-28016-0_2

Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems 2023

DOI: 10.1007/978-3-031-28016-0_2

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Evolvement of Scheduling Theories for Autonomous Vehicles

Wanli Chang,

Nan Chen,

Shuai Zhao

et al.

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Cited by 2 publications

References 51 publications

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Transformer-Based Unified Segmentation: Simultaneous Semantic and Instance Tasks on Point Clouds

Pan,

Luan,

et al. 2023

J CIRCUIT SYST COMP

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Traditionally, 3D segmentation tasks have operated in silos, focusing separately on semantic and instance segmentation. However, this disjointed approach lacks interoperability and fails to fully unleash the potential of a more integrated, multitask solution. To overcome this limitation, we introduce TUS-Net, an innovative transformer-based architecture meticulously crafted for both semantic and instance segmentation of point clouds. Our model introduces two pivotal advancements: First, it employs a superpoint-based pre-processing step that minimizes computational overhead without compromising on precision. Second, we leverage a dual-branch design within the transformer architecture, allowing it to adapt to the nuances of both segmentation tasks dynamically. Through extensive experimentation on the ScanNet dataset, our findings demonstrate that TUS-Net surpasses prevailing specialized models by a substantial margin and maintains remarkable computational efficiency. Notably, we achieve a 5.7% enhancement in mean Average Precision (mAP), for instance, segmentation, while striking an optimal balance between accuracy and runtime for semantic segmentation. These outcomes underscore the versatility, efficiency and high-performance attributes of TUS-Net, positioning it as an indispensable framework for robust 3D point cloud segmentation.

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Transformer-Based Unified Segmentation: Simultaneous Semantic and Instance Tasks on Point Clouds

Pan,

Luan,

et al. 2023

J CIRCUIT SYST COMP

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Robust 3D Point Cloud Recognition: Enhancing Robustness with GPT-4 and CLIP Integration

Pan,

Li,

Luan

et al. 2024

J CIRCUIT SYST COMP

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In recent years, deep neural networks have achieved significant success in 3D point cloud recognition tasks. However, these models still demonstrate substantial performance challenges in the presence of data corruption. It is crucial to improve the robustness and generalization ability of the model. In this work, we propose a novel framework that combines GPT and CLIP models to enhance the robustness of existing point cloud classification models, which has two main modules: the Text-Image Fusion Module, which includes a GPT-Driven TextGen Processor and FocalView Projection, and the Dual-Path Intelligent Adapter Module. First, the GPT-Driven TextGen Processor leverages GPT-4’s capabilities to generate detailed textual descriptions tailored to point cloud intricacies. FocalView Projection dynamically selects viewpoints based on attention maps, enhancing two-dimensional representations of three-dimensional point clouds. Secondly, the Dual-Path Intelligent Adapter Module achieves fine-tuning and feature adaptation by combining internal and external adapters. Additionally, during the fine-tuning process, we employ a variant of Projected Gradient Descent (PGD) adversarial training, named VPGD, to increase the model’s resilience to adversarial perturbations. Our approach has achieved state-of-the-art results on robust 3D points cloud recognition datasets such as ModelNet40-C and ScanObjectNN-C.

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Evolvement of Scheduling Theories for Autonomous Vehicles

Cited by 2 publications

References 51 publications

Transformer-Based Unified Segmentation: Simultaneous Semantic and Instance Tasks on Point Clouds

Transformer-Based Unified Segmentation: Simultaneous Semantic and Instance Tasks on Point Clouds

Robust 3D Point Cloud Recognition: Enhancing Robustness with GPT-4 and CLIP Integration

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