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

Zhang et al., 2023 - Google Patents

ForceFormer: exploring social force and transformer for pedestrian trajectory prediction

Zhang et al., 2023

View PDF
Document ID
1423055044582705506
Author
Zhang W
Cheng H
Johora F
Sester M
Publication year
Publication venue
2023 IEEE Intelligent Vehicles Symposium (IV)

External Links

Snippet

Predicting trajectories of pedestrians based on goal information in highly interactive scenes is a crucial step toward Intelligent Transportation Systems and Autonomous Driving. The challenges of this task come from two key sources:(1) complex social interactions in high …
Continue reading at arxiv.org (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
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • 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
    • 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
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • 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
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals

Similar Documents

Publication Publication Date Title
Li et al. Grip++: Enhanced graph-based interaction-aware trajectory prediction for autonomous driving
US20230127115A1 (en) Three-Dimensional Object Detection
Ivanovic et al. Mats: An interpretable trajectory forecasting representation for planning and control
Yang et al. A novel graph-based trajectory predictor with pseudo-oracle
Zhang et al. ForceFormer: exploring social force and transformer for pedestrian trajectory prediction
Bharilya et al. Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
Murali et al. Utilizing semantic visual landmarks for precise vehicle navigation
Roh et al. Multimodal trajectory prediction via topological invariance for navigation at uncontrolled intersections
Gilitschenski et al. Deep context maps: Agent trajectory prediction using location-specific latent maps
Yang et al. PTPGC: Pedestrian trajectory prediction by graph attention network with ConvLSTM
Shen et al. Parkpredict+: Multimodal intent and motion prediction for vehicles in parking lots with cnn and transformer
Zhou et al. Static-dynamic global graph representation for pedestrian trajectory prediction
Fang et al. Heterogeneous trajectory forecasting via risk and scene graph learning
Wang et al. Deep understanding of big geospatial data for self-driving: Data, technologies, and systems
Mo et al. Predictive neural motion planner for autonomous driving using graph networks
Habibi et al. SILA: An incremental learning approach for pedestrian trajectory prediction
Xiang et al. Map-free trajectory prediction in traffic with multi-level spatial-temporal modeling
Wang et al. A multi-modal spatial–temporal model for accurate motion forecasting with visual fusion
Yang et al. IST-PTEPN: an improved pedestrian trajectory and endpoint prediction network based on spatio-temporal information
Katuwandeniya et al. Multi-modal scene-compliant user intention estimation in navigation
Liu et al. An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning
Forechi et al. Sequential appearance-based Global Localization using an ensemble of kNN-DTW classifiers
Zhou et al. An autonomous navigation approach for unmanned vehicle in off-road environment with self-supervised traversal cost prediction
Kaiser et al. Investigations on pedestrian long-term trajectory prediction based on AI and environmental maps
Dulian et al. Exploiting latent representation of sparse semantic layers for improved short-term motion prediction with Capsule Networks