Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder

• URL: http://arxiv.org/abs/2105.01924v1
• Date: Wed, 5 May 2021 08:24:03 GMT
• Title: Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder
• Authors: Jonas Wurst, Lakshman Balasubramanian, Michael Botsch and Wolfgang Utschick
• Abstract summary: A method to detect novel traffic scenarios based on their infrastructure images is presented. An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection. The presented method outperforms other state-of-the-art outlier detection approaches.
• Score: 12.194597074511863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting unknown and untested scenarios is crucial for scenario-based testing. Scenario-based testing is considered to be a possible approach to validate autonomous vehicles. A traffic scenario consists of multiple components, with infrastructure being one of it. In this work, a method to detect novel traffic scenarios based on their infrastructure images is presented. An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection. The triplet training of the network is based on the connectivity graphs of the infrastructure. By using the proposed architecture, expert-knowledge is used to shape the latent space such that it incorporates a pre-defined similarity in the neighborhood relationships of an autoencoder. An ablation study on the architecture is highlighting the importance of the triplet autoencoder combination. The best performing architecture is based on vision transformers, a convolution-free attention-based network. The presented method outperforms other state-of-the-art outlier detection approaches.

Related papers

• Traffic Light Recognition using Convolutional Neural Networks: A Survey [4.451479907610764]
  We conduct a comprehensive survey and analysis of traffic light recognition methods that use convolutional neural networks (CNNs) Based on an underlying architecture, we cluster methods into three major groups. We describe the most important works in each cluster, discuss the usage of the datasets, and identify research gaps.
  arXiv  Detail & Related papers  (2023-09-05T11:50:38Z)
• Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
  We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
  arXiv  Detail & Related papers  (2023-07-01T13:53:14Z)
• OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
  We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
  arXiv  Detail & Related papers  (2023-04-20T16:31:22Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Collaborative 3D Object Detection for Automatic Vehicle Systems via Learnable Communications [8.633120731620307]
  We propose a novel collaborative 3D object detection framework that consists of three components. Experiment results and bandwidth usage analysis demonstrate that our approach can save communication and computation costs.
  arXiv  Detail & Related papers  (2022-05-24T07:17:32Z)
• A Hierarchical Terminal Recognition Approach based on Network Traffic Analysis [0.48298211429517085]
  We propose a hierarchical terminal recognition approach that applies the details of grid data. We have formed a two-level model structure by segmenting the grid data. Through the selection and reconstruction of features, we combine three algorithms to achieve accurate identification of terminal types.
  arXiv  Detail & Related papers  (2022-04-16T05:33:01Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• MD-CSDNetwork: Multi-Domain Cross Stitched Network for Deepfake Detection [80.83725644958633]
  Current deepfake generation methods leave discriminative artifacts in the frequency spectrum of fake images and videos. We present a novel approach, termed as MD-CSDNetwork, for combining the features in the spatial and frequency domains to mine a shared discriminative representation.
  arXiv  Detail & Related papers  (2021-09-15T14:11:53Z)
• Intelligent Railway Foreign Object Detection: A Semi-supervised Convolutional Autoencoder Based Method [7.557470133155959]
  We develop a semi-supervised convolutional autoencoder based framework that only requires railway track images without prior knowledge on the foreign objects in the training process. The proposed framework is useful for data analytics via the train Internet-of-Things (IoT) systems.
  arXiv  Detail & Related papers  (2021-08-05T07:32:23Z)
• Parsing-based View-aware Embedding Network for Vehicle Re-Identification [138.11983486734576]
  We propose a parsing-based view-aware embedding network (PVEN) to achieve the view-aware feature alignment and enhancement for vehicle ReID. The experiments conducted on three datasets show that our model outperforms state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-04-10T13:06:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.