CoLD Fusion: A Real-time Capable Spline-based Fusion Algorithm for Collective Lane Detection

• URL: http://arxiv.org/abs/2512.14355v1
• Date: Tue, 16 Dec 2025 12:34:17 GMT
• Title: CoLD Fusion: A Real-time Capable Spline-based Fusion Algorithm for Collective Lane Detection
• Abstract summary: Comprehensive environment perception is essential for autonomous vehicles to operate safely.<n>We propose a real-time capable approach for collective perception of lanes using a spline-based estimation of undetected road sections.<n>We were able to achieve real-time capability and extend the perception range by up to 200%.
• Score: 0.5701177763922466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Comprehensive environment perception is essential for autonomous vehicles to operate safely. It is crucial to detect both dynamic road users and static objects like traffic signs or lanes as these are required for safe motion planning. However, in many circumstances a complete perception of other objects or lanes is not achievable due to limited sensor ranges, occlusions, and curves. In scenarios where an accurate localization is not possible or for roads where no HD maps are available, an autonomous vehicle must rely solely on its perceived road information. Thus, extending local sensing capabilities through collective perception using vehicle-to-vehicle communication is a promising strategy that has not yet been explored for lane detection. Therefore, we propose a real-time capable approach for collective perception of lanes using a spline-based estimation of undetected road sections. We evaluate our proposed fusion algorithm in various situations and road types. We were able to achieve real-time capability and extend the perception range by up to 200%.

Related papers

• Multi-Modal Traffic Sign Detection with Semantic Attributes for Autonomous Driving [36.63134402686799]
  Vision-based methods face three fundamental limitations: poor cross-regional generalization due to high diversity across countries, degraded performance on small-object detection at long ranges, and fragile temporal tracking under strongly non-linear perspective distortion that occurs as a vehicle approaches a sign.<n>We present a multi-modal detection framework whose Intensity-Aware Deformable Fusion module aligns retro-reflective LiDAR cues with camera features, anchoring detection on geometric invariants rather than region-specific visual appearance.<n>Extensive evaluation on our dataset, spanning 60+ countries and 2,500+ hours of driving data, shows that the proposed pipeline achieves an Object Miss Ratio (
  arXiv  Detail & Related papers  (2026-08-21T08:51:39Z)
• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving [33.609780917199394]
  Self-driving vehicles must understand its environment to determine appropriate action. Traditional systems rely on object detection to find agents in the scene. We present a unified, interpretable, and efficient autonomy framework that moves away from cascading modules that first perceive occupancy relevant-temporal autonomy.
  arXiv  Detail & Related papers  (2024-04-01T21:11:43Z)
• MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles [58.461077944514564]
  This paper presents MSight, a cutting-edge roadside perception system specifically designed for automated vehicles. MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction. Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency.
  arXiv  Detail & Related papers  (2023-10-08T21:32:30Z)
• 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)
• Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving [58.879758550901364]
  Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context. We introduce a framework that integrates three cameras to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset.
  arXiv  Detail & Related papers  (2022-10-13T05:56:20Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
  We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
  arXiv  Detail & Related papers  (2021-04-19T11:48:13Z)
• Risk-Aware Lane Selection on Highway with Dynamic Obstacles [18.24314781032556]
  We propose a real-time lane-selection algorithm with careful cost considerations and with modularity in design. The algorithm is a search-based optimization method that evaluates uncertain dynamic positions of other vehicles under a continuous time and space domain.
  arXiv  Detail & Related papers  (2021-04-08T22:54:27Z)
• Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles [10.485790589381704]
  Trajectory prediction is crucial for autonomous vehicles. We propose a novel instance-aware representation for lane representation. We show that the proposed lane representation together with the lane attention module can be integrated into the widely used encoder-decoder framework.
  arXiv  Detail & Related papers  (2020-07-06T07:57:23Z)
• Where can I drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving [2.378161932344701]
  We propose to classify specifically a drivable corridor of the ego lane on pixel level with a deep learning approach. Our approach is kept computationally efficient with only 0.66 million parameters allowing its application in large scale products.
  arXiv  Detail & Related papers  (2020-04-16T13:04:18Z)
• PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving [8.105493956485583]
  We use a conditional imitation learning algorithm to predict trajectories for ego vehicle and its neighbors. Our approach is computationally efficient and relies only on on-board sensors. We evaluate our method offline on the publicly available dataset nuScenes.
  arXiv  Detail & Related papers  (2020-03-09T16:55:07Z)

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.