[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zhong, 2023 - Google Patents

Occlusion-aware Perception and Planning for Automated Vehicles

Zhong, 2023

View PDF
Document ID
13429403682130555843
Author
Zhong Y
Publication year

External Links

Snippet

The perception system is a key component of automated vehicles, as it relies on onboard sensors to gather information. However, the system faces challenges due to occlusions that obstruct visibility. Safely navigating in highly occluded scenarios remains a significant …
Continue reading at deepblue.lib.umich.edu (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects

Similar Documents

Publication Publication Date Title
Zeng et al. Dsdnet: Deep structured self-driving network
Bachute et al. Autonomous driving architectures: insights of machine learning and deep learning algorithms
Ma et al. Artificial intelligence applications in the development of autonomous vehicles: A survey
US11537134B1 (en) Generating environmental input encoding for training neural networks
Golchoubian et al. Pedestrian trajectory prediction in pedestrian-vehicle mixed environments: A systematic review
EP3959576A1 (en) Methods and systems for trajectory forecasting with recurrent neural networks using inertial behavioral rollout
Choi et al. DROGON: A trajectory prediction model based on intention-conditioned behavior reasoning
Sharma et al. Pedestrian intention prediction for autonomous vehicles: A comprehensive survey
Bharilya et al. Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
Kolekar et al. Behavior prediction of traffic actors for intelligent vehicle using artificial intelligence techniques: A review
Benrachou et al. Use of social interaction and intention to improve motion prediction within automated vehicle framework: A review
US20230311932A1 (en) Merging object and background radar data for autonomous driving simulations
Lodhi et al. Autonomous vehicular overtaking maneuver: A survey and taxonomy
Chen et al. Data-driven Traffic Simulation: A Comprehensive Review
Wang et al. Deep understanding of big geospatial data for self-driving: Data, technologies, and systems
Zhou et al. Spatiotemporal attention-based pedestrian trajectory prediction considering traffic-actor interaction
US20230311930A1 (en) Capturing and simulating radar data for autonomous driving systems
Villagra et al. Motion prediction and risk assessment
Zhong Occlusion-aware Perception and Planning for Automated Vehicles
US20240351592A1 (en) Support tools for av testing
Schörner et al. Grid-based micro traffic prediction using fully convolutional networks
Li et al. How Far Ahead Should Autonomous Vehicles Start Resolving Predicted Conflicts? Exploring Uncertainty-Based Safety-Efficiency Trade-Off
Dey et al. Machine learning based perception architecture design for semi-autonomous vehicles
Huang Safe intention-aware maneuvering of autonomous vehicles
Zhang et al. Finding critical scenarios for automated driving systems: The data extraction form