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

Sauer et al., 2018 - Google Patents

Conditional affordance learning for driving in urban environments

Sauer et al., 2018

View PDF
Document ID
6169039130210721704
Author
Sauer A
Savinov N
Geiger A
Publication year
Publication venue
Conference on robot learning

External Links

Snippet

Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm …
Continue reading at proceedings.mlr.press (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
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/16Anti-collision systems
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • 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

Similar Documents

Publication Publication Date Title
Sauer et al. Conditional affordance learning for driving in urban environments
Elallid et al. A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving
Zhan et al. Interaction dataset: An international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps
Yu et al. Occlusion-aware risk assessment for autonomous driving in urban environments
Cai et al. DQ-GAT: Towards safe and efficient autonomous driving with deep Q-learning and graph attention networks
Katrakazas et al. Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions
Huang et al. Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving
Reda et al. Path planning algorithms in the autonomous driving system: A comprehensive review
US11433922B1 (en) Object uncertainty detection
Xia et al. Parameterized Decision-making with Multi-modal Perception for Autonomous Driving
CN117794803A (en) Vehicle trajectory control using tree search and/or proxy filtering with responsive proxy prediction
Chen et al. Vadv2: End-to-end vectorized autonomous driving via probabilistic planning
Zhang et al. Trajectory planning based on spatio-temporal map with collision avoidance guaranteed by safety strip
Kamran et al. Minimizing safety interference for safe and comfortable automated driving with distributional reinforcement learning
Wang et al. Imitation learning of hierarchical driving model: from continuous intention to continuous trajectory
Zhang et al. Lexicographic actor-critic deep reinforcement learning for urban autonomous driving
Eraqi et al. Dynamic conditional imitation learning for autonomous driving
Meng et al. Reactive and safe road user simulations using neural barrier certificates
Guo et al. Toward human-like behavior generation in urban environment based on Markov decision process with hybrid potential maps
Ren et al. Self-learned intelligence for integrated decision and control of automated vehicles at signalized intersections
Siboo et al. An empirical study of ddpg and ppo-based reinforcement learning algorithms for autonomous driving
Wang et al. Vision-based autonomous driving: A hierarchical reinforcement learning approach
US20240101150A1 (en) Conditional trajectory determination by a machine learned model
Ma et al. Data-driven state-increment statistical model and its application in autonomous driving
Trumpp et al. Efficient Learning of Urban Driving Policies Using Bird'View State Representations