Nasernejad et al., 2023 - Google Patents
Multiagent modeling of pedestrian-vehicle conflicts using Adversarial Inverse Reinforcement LearningNasernejad et al., 2023
- Document ID
- 17227087570334829125
- Author
- Nasernejad P
- Sayed T
- Alsaleh R
- Publication year
- Publication venue
- Transportmetrica A: transport science
External Links
Snippet
There is a need for a better understanding of the collision avoidance behavior of road users in near misses. Recently, several models of road user behavior in near misses have been proposed. However, despite the multiagent nature of road user interactions, most of these …
- 230000002787 reinforcement 0 title description 31
Classifications
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- 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
- 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
- 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
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
-
- 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
- G06Q30/00—Commerce, e.g. shopping or e-commerce
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Nasernejad et al. | Multiagent modeling of pedestrian-vehicle conflicts using Adversarial Inverse Reinforcement Learning | |
Xu et al. | Trajectory prediction for heterogeneous traffic-agents using knowledge correction data-driven model | |
Tageldin et al. | Models to evaluate the severity of pedestrian-vehicle conflicts in five cities | |
US11794731B2 (en) | Waypoint prediction for vehicle motion planning | |
Alsaleh et al. | Microscopic modeling of cyclists interactions with pedestrians in shared spaces: a Gaussian process inverse reinforcement learning approach | |
Munigety et al. | Towards behavioral modeling of drivers in mixed traffic conditions | |
Alsaleh et al. | Markov-game modeling of cyclist-pedestrian interactions in shared spaces: A multi-agent adversarial inverse reinforcement learning approach | |
Choi et al. | Drogon: A causal reasoning framework for future trajectory forecast | |
Guo et al. | Lane change detection and prediction using real-world connected vehicle data | |
Mohammed et al. | Microscopic modeling of cyclists on off-street paths: a stochastic imitation learning approach | |
Hu et al. | Vehicle trajectory prediction considering aleatoric uncertainty | |
Benrachou et al. | Use of social interaction and intention to improve motion prediction within automated vehicle framework: A review | |
Cong et al. | DACR-AMTP: adaptive multi-modal vehicle trajectory prediction for dynamic drivable areas based on collision risk | |
Lanzaro et al. | Can motorcyclist behavior in traffic conflicts be modeled? A deep reinforcement learning approach for motorcycle-pedestrian interactions | |
Liu et al. | Modelling motorized and non-motorized vehicle conflicts using multiagent inverse reinforcement learning approach | |
Papathanasopoulou et al. | Data-driven traffic simulation models: Mobility patterns using machine learning techniques | |
Alsaleh et al. | Do road users play Nash Equilibrium? A comparison between Nash and Logistic stochastic Equilibriums for multiagent modeling of road user interactions in shared spaces | |
Fu et al. | A method in modeling interactive pedestrian crossing and driver yielding decisions during their interactions at intersections | |
Johora et al. | On the generalizability of motion models for road users in heterogeneous shared traffic spaces | |
Wang et al. | Deep understanding of big geospatial data for self-driving: Data, technologies, and systems | |
Lanzaro et al. | Modeling motorcyclist–pedestrian near misses: A multiagent adversarial inverse reinforcement learning approach | |
Nidamanuri et al. | Auto-alert: A spatial and temporal architecture for driving assistance in road traffic environments | |
Zeng et al. | Modeling vehicle U-turning behavior near intersections: A deep learning approach based on TCN and multi-head attention | |
Dong et al. | An enhanced motion planning approach by integrating driving heterogeneity and long-term trajectory prediction for automated driving systems: A highway merging case study | |
Fu et al. | Summary and reflections on pedestrian trajectory prediction in the field of autonomous driving |