Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges
Semantic segmentation was seen as a challenging computer vision problem few years ago. Due to recent advancements in deep learning, relatively accurate solutions are now possible for its use in automated driving. In this paper, the semantic segmentation problem is explored from the perspective of automated driving. Most of the current semantic segmentation algorithms are designed for generic images and do not incorporate prior structure and end goal for automated driving. First, the paper begins with a generic taxonomic survey of semantic segmentation algorithms and then discusses how it fits in the context of automated driving. Second, the particular challenges of deploying it into a safety system which needs high level of accuracy and robustness are listed. Third, different alternatives instead of using an independent semantic segmentation module are explored. Finally, an empirical evaluation of various semantic segmentation architectures was performed on CamVid dataset in terms of accuracy and speed. This paper is a preliminary shorter version of a more detailed survey which is work in progress.
Code (0)
등록된 구현이 없습니다.
Tasks
SegmentationSemantic SegmentationSurveySimilar Papers 제목 키워드 기반
Latent World Models for Automated Driving: A Unified Taxonomy, Evaluation Framework, and Open Challenges
Emerging generative world models and vision-language-action (VLA) systems are rapidly reshaping automated driving by enabling scalable simulation, long-horizon forecasting, and capability-rich decision making. Across the…
Decision MakingForging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems
The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor data. While foundation models excel in …
Computational Efficiency3D Object DetectionAuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving
Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the car. Pixel level classification once con…
Decision MakingDepth EstimationDomain AdaptationMulti-Task Learning+2End-To-End multi-modal sensors fusion system for urban automated driving
In this paper, we present a novel framework for urban automated driving based on multi-modal sensors; LiDAR and Camera. Environment perception through sensors fusion is key to successful deployment of automated driving s…
SegmentationSemantic SegmentationTaxonomy-Aware Continual Semantic Segmentation in Hyperbolic Spaces for Open-World Perception
Semantic segmentation models are typically trained on a fixed set of classes, limiting their applicability in open-world scenarios. Class-incremental semantic segmentation aims to update models with emerging new classes …
Autonomous DrivingClass-Incremental Semantic SegmentationContinual Semantic SegmentationIncremental Learning+2