[go: up one dir, main page]

Garg, 2020 - Google Patents

Autonomous traffic signal control using deep reinforcement learning

Garg, 2020

View PDF
Document ID
11504187546482567957
Author
Garg D
Publication year

External Links

Snippet

Traffic signals provide one of the primary means to administer conflicting road traffic flows. The efficiency of road transportation systems significantly depends on signal operation. The state of-the-art signal control strategies are unable to efficiently and autonomously adapt to …
Continue reading at publications.aston.ac.uk (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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only

Similar Documents

Publication Publication Date Title
Garg et al. Deep reinforcement learning for autonomous traffic light control
Sheng et al. Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving
Chu et al. Traffic signal control using end-to-end off-policy deep reinforcement learning
Wang et al. Improved multi-agent reinforcement learning for path planning-based crowd simulation
Casas Deep deterministic policy gradient for urban traffic light control
Jin et al. An intelligent control system for traffic lights with simulation-based evaluation
Chang et al. BEV-V2X: Cooperative birds-eye-view fusion and grid occupancy prediction via V2X-based data sharing
CN113257016B (en) Traffic signal control method and device and readable storage medium
Roca et al. Emergent behaviors in the internet of things: The ultimate ultra-large-scale system
CN119416626A (en) Intelligent connected vehicle dynamics simulation method and system based on physical engine
Krzysztof The importance of automatic traffic lights time algorithms to reduce the negative impact of transport on the urban environment
CN112947466B (en) Parallel planning method and equipment for automatic driving and storage medium
Sadid et al. Dynamic spatio-temporal graph neural network for surrounding-aware trajectory prediction of autonomous vehicles
Jaleel et al. Reducing congestion in an intelligent traffic system with collaborative and adaptive signaling on the edge
Jiang et al. A general scenario-agnostic reinforcement learning for traffic signal control
Jácome et al. A survey on intelligent traffic lights
Garg et al. A deep reinforcement learning agent for traffic intersection control optimization
Xu et al. Integration of mixture of experts and multimodal generative ai in internet of vehicles: A survey
Mokhtari et al. Safe deep q-network for autonomous vehicles at unsignalized intersection
Zhang et al. PlanLight: Learning to optimize traffic signal control with planning and iterative policy improvement
Lei et al. Digital twin‐based multi‐objective autonomous vehicle navigation approach as applied in infrastructure construction
Wang et al. Traffic signal cycle control with centralized critic and decentralized actors under varying intervention frequencies
Jiang et al. A deep-learning-based approach for simulating pedestrian turning flow
CN118790287B (en) Automatic driving decision method and system based on generated world model and multi-step reinforcement learning
CN118928464A (en) Method and device for generating automatic driving decision based on hybrid expert model