Technology Category
Planning, Routing, and Scheduling: Planning with Language ModelsComputer Vision: Diffusion Models for VisionMachine Learning: Structured Learning
PriorFusion: Unified Integration of Priors for Robust Road Perception in Autonomous Driving
🤖 AI Summary
In complex urban driving scenarios lacking high-definition maps, existing methods suffer from insufficient utilization of structural road priors, resulting in irregular and brittle predictions. To address this, we propose a unified road perception framework that jointly integrates semantic, geometric, and generative priors. Our approach introduces three key innovations: (1) an instance-aware, shape-guided attention mechanism that explicitly models geometric structure during feature aggregation; (2) a data-driven shape template space constructed via clustering, enabling compact, low-dimensional anchor priors for robust road element representation; and (3) a diffusion-based structured generation paradigm that enforces geometric consistency and regularity in output predictions. Evaluated on large-scale autonomous driving benchmarks, our method achieves significant improvements in road element detection accuracy. Notably, it demonstrates superior robustness and prediction consistency under challenging conditions—including severe occlusion and rapid curvature variation—while maintaining strong generalization across diverse urban layouts.
Technology Category
Application Category
📝 Abstract
With the growing interest in autonomous driving, there is an increasing demand for accurate and reliable road perception technologies. In complex environments without high-definition map support, autonomous vehicles must independently interpret their surroundings to ensure safe and robust decision-making. However, these scenarios pose significant challenges due to the large number, complex geometries, and frequent occlusions of road elements. A key limitation of existing approaches lies in their insufficient exploitation of the structured priors inherently present in road elements, resulting in irregular, inaccurate predictions. To address this, we propose PriorFusion, a unified framework that effectively integrates semantic, geometric, and generative priors to enhance road element perception. We introduce an instance-aware attention mechanism guided by shape-prior features, then construct a data-driven shape template space that encodes low-dimensional representations of road elements, enabling clustering to generate anchor points as reference priors. We design a diffusion-based framework that leverages these prior anchors to generate accurate and complete predictions. Experiments on large-scale autonomous driving datasets demonstrate that our method significantly improves perception accuracy, particularly under challenging conditions. Visualization results further confirm that our approach produces more accurate, regular, and coherent predictions of road elements.
Problem
Research questions and friction points this paper is trying to address.
Enhancing road perception in autonomous driving without HD maps
Integrating semantic, geometric, and generative priors for accuracy
Overcoming occlusion and complexity challenges in road elements
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates semantic, geometric, and generative priors
Uses instance-aware attention with shape-prior features
Employs diffusion-based framework for accurate predictions
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