Hierarchical Road Topology Learning for Urban Map-less Driving
The majority of current approaches in autonomous driving rely on High-Definition (HD) maps which detail the road geometry and surrounding area. Yet, this reliance is one of the obstacles to mass deployment of autonomous vehicles due to poor scalability of such prior maps. In this paper, we tackle the problem of online road map extraction via leveraging the sensory system aboard the vehicle itself. To this end, we design a structured model where a graph representation of the road network is generated in a hierarchical fashion within a fully convolutional network. The method is able to handle complex road topology and does not require a user in the loop.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingAutonomous VehiclesSimilar Papers 제목 키워드 기반
Optimal Driving Model for Connected and Automated Electric Freight Vehicles in a Wireless Charging Scenario at Signalised Intersections
Electric freight vehicles have become an important means of transportation in connected and automated environments owing to their numerous advantages. However, the generally short driving range of connected and automated…
Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images
Self-driving vehicles rely on urban street maps for autonomous navigation. In this paper, we introduce Pix2Map, a method for inferring urban street map topology directly from ego-view images, as needed to continually upd…
Autonomous NavigationCross-Modal RetrievalImage RetrievalRetrieval+1Topology Preserving Local Road Network Estimation from Single Onboard Camera Image
Knowledge of the road network topology is crucial for autonomous planning and navigation. Yet, recovering such topology from a single image has only been explored in part. Furthermore, it needs to refer to the ground pla…
Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection
Road inspection is essential for ensuring road maintenance and traffic safety, as road defects gradually emerge and compromise road functionality. Traditional methods, which rely on manual evaluations, are labor-intensiv…
TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations
Topology reasoning is crucial for autonomous driving. Current methods primarily focus on instance-level learning for centerline detection, followed by a sequential module for topology reasoning that relies on simplified …
Autonomous Driving