The navigation of an AV in an environment may be planned based on a model of a restricted traffic zone in the environment. Information of the environment (e.g., a vector map, information of one or more objects, a temporal sequence of semantic grids, a query grid, etc.) may be input into a neural network. The neural network may include a CNN and a GNN. The map and tracks may be input into the GNN. The temporal sequence of semantic grids or query grid may be input into the CNN. The neural network may output edges of the restricted traffic zone. The neural network may output a grid of points representing locations in the environment and information indicating drivability of each respective point. The neural network may output one or more polylines dividing the environment into regions and information indicating whether the AV can drive to or in each respective region.
USING NEURAL NETWORKS TO MODEL RESTRICTED TRAFFIC ZONES FOR AUTONOMOUS VEHICLE NAVIGATION
2024-10-31
Patent
Electronic Resource
English
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G01C Messen von Entfernungen, Höhen, Neigungen oder Richtungen , MEASURING DISTANCES, LEVELS OR BEARINGS / G05B Steuer- oder Regelsysteme allgemein , CONTROL OR REGULATING SYSTEMS IN GENERAL |
SYSTEMS AND METHODS FOR DETECTING RESTRICTED TRAFFIC ZONES FOR AUTONOMOUS DRIVING
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