Hu et al., 2022 - Google Patents
A roadside decision-making methodology based on deep reinforcement learning to simultaneously improve the safety and efficiency of merging zoneHu et al., 2022
- Document ID
- 14832512488431663697
- Author
- Hu J
- Li X
- Cen Y
- Xu Q
- Zhu X
- Hu W
- Publication year
- Publication venue
- IEEE Transactions on Intelligent Transportation Systems
External Links
Snippet
The safety and efficiency of the merging zone is particularly important for traffic networks. Although autonomous vehicle improves the safety and efficiency from vehicle view, traffic controlling in merging zone mostly focus on improving efficiency from roadside view …
- 238000000034 method 0 title abstract description 25
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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
-
- 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
- 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
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ding et al. | Epsilon: An efficient planning system for automated vehicles in highly interactive environments | |
US20230124864A1 (en) | Graph Representation Querying of Machine Learning Models for Traffic or Safety Rules | |
US11537134B1 (en) | Generating environmental input encoding for training neural networks | |
Ding et al. | Online vehicle trajectory prediction using policy anticipation network and optimization-based context reasoning | |
Morton et al. | Analysis of recurrent neural networks for probabilistic modeling of driver behavior | |
Wu et al. | Emergent behaviors in mixed-autonomy traffic | |
Baskar et al. | Traffic control and intelligent vehicle highway systems: a survey | |
Gangopadhyay et al. | Hierarchical program-triggered reinforcement learning agents for automated driving | |
Hu et al. | A roadside decision-making methodology based on deep reinforcement learning to simultaneously improve the safety and efficiency of merging zone | |
Weng et al. | Modeling vehicle merging behavior in work zone merging areas during the merging implementation period | |
Fors et al. | Resilient branching MPC for multi-vehicle traffic scenarios using adversarial disturbance sequences | |
Yang et al. | Prediction failure risk-aware decision-making for autonomous vehicles on signalized intersections | |
Liao et al. | A game theory based ramp merging strategy for connected and automated vehicles in the mixed traffic: A unity-sumo integrated platform | |
Menendez-Romero et al. | Courtesy behavior for highly automated vehicles on highway interchanges | |
Zhou et al. | Identify, estimate and bound the uncertainty of reinforcement learning for autonomous driving | |
Zhao et al. | Genetic algorithm-based SOTIF scenario construction for complex traffic flow | |
Farrag et al. | STIMF: a smart traffic incident management framework | |
Liu et al. | Graph reinforcement learning application to co-operative decision-making in mixed autonomy traffic: Framework, survey, and challenges | |
Chen et al. | Risk-anticipatory autonomous driving strategies considering vehicles’ weights based on hierarchical deep reinforcement learning | |
Jeong | Probabilistic game theory and stochastic model predictive control-based decision making and motion planning in uncontrolled intersections for autonomous driving | |
Katrakazas | Developing an advanced collision risk model for autonomous vehicles | |
Huang et al. | Digital twin edge services with proximity-aware longitudinal lane changing model for connected vehicles | |
Ma et al. | Evolving testing scenario generation method and intelligence evaluation framework for automated vehicles | |
Cheng et al. | Architecting dependable learning-enabled autonomous systems: A survey | |
Molaie et al. | Auto-driving policies in highway based on distributional deep reinforcement learning |