IntersectionControl
An environment-agnostic framework for comparing intersection control algorithms
Install / Use
/learn @julesdehon/IntersectionControlREADME
IntersectionControl
:warning: This repository is under active development for my BEng thesis and there is a high chance many APIs and components will change in the near future
An environment-agnostic framework for implementing and comparing intersection control algorithms
<img alt="Algorithm Environment Interaction" src="docs/source/image/flowcharts/algo-env-relationship.png" width="300px"/>
Getting Started
Installation
$ pip install intersection-control
Refer to the documentation for all installation options
Usage
For a more detailed description of various use-cases, please refer to the documentation.
To run a simple experiment using the QBIM algorithm and SumoEnvironment:
Import the desired algorithm/environment:
from intersection_control.environments.sumo import SumoEnvironment, RandomDemandGenerator
from intersection_control.algorithms.qb_im import QBIMIntersectionManager, QBIMVehicle
Instantiate the environment:
The RandomDemandGenerator here is used to programmatically add vehicles to specifically to the Sumo environment.
Alternatively, Sumo based [demand generation](https://sumo.dlr.de/docs/Demand/Introduction_to_demand_modelling_in_SUMO.html)
could be used
demand_generator = RandomDemandGenerator({
"NE": 2, "NS": 2, "NW": 2, "EN": 2, "ES": 2, "EW": 2,
"SN": 2, "SE": 2, "SW": 2, "WN": 2, "WE": 2, "WS": 2
}, 0.05)
env = SumoEnvironment("path/to/intersection.sumocfg",
demand_generator=demand_generator, time_step=0.05, gui=True)
Instantiate the vehicles and intersection managers:
intersection_managers = {QBIMIntersectionManager(intersection_id, env, 10, 0.05) for intersection_id in
env.intersections.get_ids()} # In this Sumo network there is only one intersection
vehicles = {QBIMVehicle(vehicle_id, env, communication_range=75) for vehicle_id in env.vehicles.get_ids()}
Run the main loop:
STEP_COUNT = 360000 # 1 hour
for _ in range(STEP_COUNT):
env.step()
removed_vehicles = {v for v in vehicles if v.get_id() in env.get_removed_vehicles()}
for v in removed_vehicles:
v.destroy()
new_vehicles = {QBIMVehicle(vehicle_id, env, communication_range=75)
for vehicle_id in env.get_added_vehicles()}
vehicles = (vehicles - removed_vehicles).union(new_vehicles)
for vehicle in vehicles:
vehicle.step()
for intersection_manager in intersection_managers:
intersection_manager.step()
This simple example is available in misc/main.py:

Exploring the code
For a full description of the code's structure please refer to the documentation
The directory structure is as follows:
IntersectionControl
├── docs # Documentation images and files
├── intersection_control # The main source code package
│ ├── core # Defines all interfaces and defines the component structure
│ │ ├── environment # Provides an interface for any environment to implement
│ │ │ ├── environment.py # Defines the base Environment class
│ │ │ ├── intersectiont_handler.py # Defines the base IntersectionHandler class
│ │ │ └── vehicle_handler.py # Defines the base VehicleHandler class
│ │ ├── algorithm
│ │ │ ├── vehicle.py # Defines the base Vehicle class
│ │ │ └── intersection_manager.py # Defines the base IntersectionManager class
│ │ ├── communication.py # Provides an interface for communication - V2V or V2I is possible. Specifically, defines the base MessagingUnit class
│ │ └── performance_indication.py # Defines the base PerformanceIndicator class (Not yet implemented)
│ ├── algorithms # A collection of intersection control algorithm implementations (for now only QBIM). These are implementations of core.Vehicle and core.IntersectionManager
│ ├── environments # A collection of environment implementations (for now only SUMO). These are implementations of core.Environment
│ └── communication # A collection of communication implementations (for now only DistanceBasedUnit). These are implementations of core.MessagingUnit
├── test # unit tests for various components
└── misc # Miscellaneous stand-alone scripts and experiments
Related Skills
node-connect
346.8kDiagnose OpenClaw node connection and pairing failures for Android, iOS, and macOS companion apps
frontend-design
107.6kCreate distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
openai-whisper-api
346.8kTranscribe audio via OpenAI Audio Transcriptions API (Whisper).
qqbot-media
346.8kQQBot 富媒体收发能力。使用 <qqmedia> 标签,系统根据文件扩展名自动识别类型(图片/语音/视频/文件)。
