Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps
Autonomous driving has traditionally relied heavily on costly and labor-intensive High Definition (HD) maps, hindering scalability. In contrast, Standard Definition (SD) maps are more affordable and have worldwide coverage, offering a scalable alternative. In this work, we systematically explore the effect of SD maps for real-time lane-topology understanding. We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Encoder Representations from transFormers, to leverage priors in SD maps for the lane-topology prediction task. This enhancement consistently and significantly boosts (by up to 60%) lane detection and topology prediction on current state-of-the-art online map prediction methods without bells and whistles and can be immediately incorporated into any Transformer-based lane-topology method. Code is available at https://github.com/NVlabs/SMERF.
Code (1)
Tasks
Autonomous DrivingLane DetectionPredictionSimilar Papers 제목 키워드 기반
SMART: Advancing Scalable Map Priors for Driving Topology Reasoning
Topology reasoning is crucial for autonomous driving as it enables comprehensive understanding of connectivity and relationships between lanes and traffic elements. While recent approaches have shown success in perceivin…
Autonomous DrivingTopoSD: Topology-Enhanced Lane Segment Perception with SDMap Prior
Recent advances in autonomous driving systems have shifted towards reducing reliance on high-definition maps (HDMaps) due to the huge costs of annotation and maintenance. Instead, researchers are focusing on online vecto…
Autonomous DrivingSEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving
Online scene perception and topology reasoning are critical for autonomous vehicles to understand their driving environments, particularly for mapless driving systems that endeavor to reduce reliance on costly High-Defin…
Autonomous DrivingAutonomous VehiclesKeypoint DetectionScene UnderstandingRelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning
Accurate road topology reasoning is critical for autonomous driving, enabling effective navigation and adherence to traffic regulations. Central to this task are lane perception and topology reasoning. However, existing …
Autonomous DrivingContrastive LearningLane DetectionWASABI: Whole-graph Assignment-based Stabilizer for lAne topology By Inter-frame tracking
Autonomous driving requires understanding the road as a graph of drivable lanes and their connectivity, beyond the ego lane alone, to follow routes through intersections and reason about cross- and merging-traffic. Recen…
Autonomous Driving