Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving

• URL: http://arxiv.org/abs/2504.19183v1
• Date: Sun, 27 Apr 2025 10:08:54 GMT
• Title: Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving
• Authors: Mi Zheng, Guanglei Yang, Zitong Huang, Zhenhua Guo, Kevin Han, Wangmeng Zuo,
• Abstract summary: We propose a novel framework termed Segmenting Objectiveness and Task-Awareness (SOTA) for autonomous driving scenes.<n>SOTA enhances the segmentation of objectiveness through a Semantic Fusion Block (SFB) and filters anomalies irrelevant to road navigation tasks.
• Score: 46.70405993442064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the emergence of transformer-based architectures and large language models (LLMs), the accuracy of road scene perception has substantially advanced. Nonetheless, current road scene segmentation approaches are predominantly trained on closed-set data, resulting in insufficient detection capabilities for out-of-distribution (OOD) objects. To overcome this limitation, road anomaly detection methods have been proposed. However, existing methods primarily depend on image inpainting and OOD distribution detection techniques, facing two critical issues: (1) inadequate consideration of the objectiveness attributes of anomalous regions, causing incomplete segmentation when anomalous objects share similarities with known classes, and (2) insufficient attention to environmental constraints, leading to the detection of anomalies irrelevant to autonomous driving tasks. In this paper, we propose a novel framework termed Segmenting Objectiveness and Task-Awareness (SOTA) for autonomous driving scenes. Specifically, SOTA enhances the segmentation of objectiveness through a Semantic Fusion Block (SFB) and filters anomalies irrelevant to road navigation tasks using a Scene-understanding Guided Prompt-Context Adaptor (SG-PCA). Extensive empirical evaluations on multiple benchmark datasets, including Fishyscapes Lost and Found, Segment-Me-If-You-Can, and RoadAnomaly, demonstrate that the proposed SOTA consistently improves OOD detection performance across diverse detectors, achieving robust and accurate segmentation outcomes.

Related papers

• Salient Object Detection in Traffic Scene through the TSOD10K Dataset [22.615252113004402]
  Traffic Salient Object Detection (TSOD) aims to segment the objects critical to driving safety by combining semantic (e.g., collision risks) and visual saliency. Our research establishes the first foundation for safety-aware saliency analysis in intelligent transportation systems.
  arXiv  Detail & Related papers  (2025-03-21T07:21:24Z)
• Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratios [4.578773000079989]
  Current road obstacle detection methods assign a score to each pixel and apply a threshold to generate final predictions.<n>We propose a novel method that leverages segment-level features from visual foundation models and likelihood ratios to predict road obstacles directly.<n>By focusing on segments rather than individual pixels, our approach enhances detection accuracy, reduces false positives, and offers increased robustness to scene variability.
  arXiv  Detail & Related papers  (2024-12-07T17:40:20Z)
• OoDIS: Anomaly Instance Segmentation and Detection Benchmark [57.89836988990543]
  This work extends some commonly used anomaly segmentation benchmarks to include the instance segmentation and object detection tasks.<n>Our evaluation of anomaly segmentation and object detection methods shows that both of these challenges remain unsolved problems.
  arXiv  Detail & Related papers  (2024-06-17T17:59:56Z)
• DARTH: Holistic Test-time Adaptation for Multiple Object Tracking [87.72019733473562]
  Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving. Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed. We introduce DARTH, a holistic test-time adaptation framework for MOT.
  arXiv  Detail & Related papers  (2023-10-03T10:10:42Z)
• Unsupervised Adaptation from Repeated Traversals for Autonomous Driving [54.59577283226982]
  Self-driving cars must generalize to the end-user's environment to operate reliably. One potential solution is to leverage unlabeled data collected from the end-users' environments. There is no reliable signal in the target domain to supervise the adaptation process. We show that this simple additional assumption is sufficient to obtain a potent signal that allows us to perform iterative self-training of 3D object detectors on the target domain.
  arXiv  Detail & Related papers  (2023-03-27T15:07:55Z)
• SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation [111.61261419566908]
  Deep neural networks (DNNs) are usually trained on a closed set of semantic classes. They are ill-equipped to handle previously-unseen objects. detecting and localizing such objects is crucial for safety-critical applications such as perception for automated driving.
  arXiv  Detail & Related papers  (2021-04-30T07:58:19Z)
• Model Guided Road Intersection Classification [2.9248680865344348]
  This work investigates inter-section classification from RGB images using well-consolidate neural network approaches along with a method to enhance the results based on the teacher/student training paradigm. An extensive experimental activity aimed at identifying the best input configuration and evaluating different network parameters on both the well-known KITTI dataset and the new KITTI-360 sequences shows that our method outperforms current state-of-the-art approaches on a per-frame basis and prove the effectiveness of the proposed learning scheme.
  arXiv  Detail & Related papers  (2021-04-26T09:15:28Z)
• Detecting 32 Pedestrian Attributes for Autonomous Vehicles [103.87351701138554]
  In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. We introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way. We show competitive detection and attribute recognition results, as well as a more stable MTL training.
  arXiv  Detail & Related papers  (2020-12-04T15:10:12Z)

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.