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

Djuric et al., 2020 - Google Patents

Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving

Djuric et al., 2020

View PDF
Document ID
3569895507286843060
Author
Djuric N
Radosavljevic V
Cui H
Nguyen T
Chou F
Lin T
Singh N
Schneider J
Publication year
Publication venue
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

External Links

Snippet

We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings. We introduce a deep learning-based approach that takes into …
Continue reading at openaccess.thecvf.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00791Recognising scenes perceived from the perspective of a land vehicle, e.g. recognising lanes, obstacles or traffic signs on road scenes
    • G06K9/00798Recognition of lanes or road borders, e.g. of lane markings, or recognition of driver's driving pattern in relation to lanes perceived from the vehicle; Analysis of car trajectory relative to detected road
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00771Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/20Analysis of motion
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/16Anti-collision systems
    • G08G1/167Driving aids for lane monitoring, lane changing, e.g. blind spot detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/20Image acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10016Video; Image sequence

Similar Documents

Publication Publication Date Title
Djuric et al. Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving
Yu et al. Occlusion-aware risk assessment for autonomous driving in urban environments
Chib et al. Recent advancements in end-to-end autonomous driving using deep learning: A survey
Li et al. End-to-end contextual perception and prediction with interaction transformer
Chou et al. Predicting motion of vulnerable road users using high-definition maps and efficient convnets
Teng et al. Hierarchical interpretable imitation learning for end-to-end autonomous driving
Cui et al. Deep kinematic models for kinematically feasible vehicle trajectory predictions
US11131993B2 (en) Methods and systems for trajectory forecasting with recurrent neural networks using inertial behavioral rollout
Hu et al. Safe local motion planning with self-supervised freespace forecasting
US11299151B2 (en) Enhanced vehicle tracking
Jebamikyous et al. Autonomous vehicles perception (avp) using deep learning: Modeling, assessment, and challenges
Cho et al. A multi-sensor fusion system for moving object detection and tracking in urban driving environments
Geiger et al. 3d traffic scene understanding from movable platforms
US10984543B1 (en) Image-based depth data and relative depth data
US11810365B1 (en) Perception error modeling
Niedoba et al. Improving movement prediction of traffic actors using off-road loss and bias mitigation
Wu et al. Crossfuser: Multi-modal feature fusion for end-to-end autonomous driving under unseen weather conditions
Xia et al. A human-like traffic scene understanding system: A survey
Kanchana et al. Computer vision for autonomous driving
Wang et al. Bevgpt: Generative pre-trained large model for autonomous driving prediction, decision-making, and planning
Herman et al. Pedestrian behavior prediction for automated driving: Requirements, metrics, and relevant features
Czech et al. On-board pedestrian trajectory prediction using behavioral features
Lyssenko et al. Towards safety-aware pedestrian detection in autonomous systems
Yang et al. Vehicle trajectory prediction based on LSTM network
Yan et al. Int2: Interactive trajectory prediction at intersections