[go: up one dir, main page]

Meftah et al., 2024 - Google Patents

Improving Autonomous Vehicles Maneuverability and Collision Avoidance in Adverse Weather Conditions Using Generative Adversarial Networks

Meftah et al., 2024

View PDF
Document ID
3705622266730522797
Author
Meftah L
Cherif A
Braham R
Publication year
Publication venue
IEEE Access

External Links

Snippet

In recent years, there has been a significant increase in the development of autonomous vehicles. One critical task for ensuring their safety and dependability, is obstacle avoidance in challenging weather conditions. However, no studies have explored the use of data …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • 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
    • G06N3/04—Architectures, e.g. interconnection topology
    • 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
    • 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
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • 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
    • 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
    • G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
    • G06K9/629—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features
    • 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
    • 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
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Boloor et al. Attacking vision-based perception in end-to-end autonomous driving models
Xiao et al. Multimodal end-to-end autonomous driving
Grigorescu et al. A survey of deep learning techniques for autonomous driving
Le Mero et al. A survey on imitation learning techniques for end-to-end autonomous vehicles
Huang et al. Multi-modal sensor fusion-based deep neural network for end-to-end autonomous driving with scene understanding
Fernando et al. Deep inverse reinforcement learning for behavior prediction in autonomous driving: Accurate forecasts of vehicle motion
Chen et al. Driving maneuvers prediction based autonomous driving control by deep Monte Carlo tree search
US20230419113A1 (en) Attention-based deep reinforcement learning for autonomous agents
Huang et al. Survey of state-of-art autonomous driving technologies with deep learning
Fernando et al. Going deeper: Autonomous steering with neural memory networks
Hu et al. Learning a deep cascaded neural network for multiple motion commands prediction in autonomous driving
CN111507160B (en) Method and apparatus for integrating travel images acquired from vehicles performing cooperative driving
Wang et al. Wsip: Wave superposition inspired pooling for dynamic interactions-aware trajectory prediction
CN112734808A (en) Trajectory prediction method for vulnerable road users in vehicle driving environment
Chen et al. Data-driven traffic simulation: A comprehensive review
Darapaneni et al. Autonomous car driving using deep learning
CN111126327B (en) Lane line detection method, system, vehicle-mounted system and vehicle
Youssef et al. Comparative study of end-to-end deep learning methods for self-driving car
Masmoudi et al. Autonomous car-following approach based on real-time video frames processing
Meftah et al. Improving Autonomous Vehicles Maneuverability and Collision Avoidance in Adverse Weather Conditions Using Generative Adversarial Networks
Zhang et al. Learning how to avoiding obstacles for end-to-end driving with conditional imitation learning
CN116448134B (en) Vehicle path planning method and device based on risk field and uncertainty analysis
EP4145242A1 (en) Perception field based driving related operations
CN116975781A (en) Automatic driving vehicle behavior decision system and method
Meftah et al. Deep residual network for autonomous vehicles obstacle avoidance