/CityFlowER

An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models

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CityFlowER: Efficient and Realistic Traffic Simulation

Documentation Status

Build Status

CityFlowER is An Efficient and Realistic Traffic Simulator Platform with Embedded Machine Learning Models. It Can Support Multi-agent Reinforcement Learning for Various Traffic Analysis and Control Tasks.

Framework and comparison to other implementations:

Framework

This simulator introduces following features:

  • Realistic A microscopic traffic simulator which simulates the behavior of each vehicle. Any individual vehicle can be controlled by one or multiple behavior models to mimic the realistic driving scenario.
Framework
  • Efficient The embedded structure's simulation speed is much faster than API based implementation, and approximating the rule based control methods.
Framework
  • Large Scale CityFlowER support large scale simulation efficiently.
Framework

  • Simple config setting for loaded models. You could find the following implementation in src/vehicle/vehicle.cpp or src/vehicle/lanechange.cpp The code blocks to import rule based file is shown below: We also provide training example in Imitate_cityflow/*, please check it out for example of training:

    // vehicleInfo is updated out of the function
// "PATH" is the pre-trained model path
double Vehicle::getCarFollowSpeed(double interval) {

Vehicle *leader = getLeader();
std::vector<torch::jit::IValue> input_feature;
auto input = torch::tensor ({leader->getSpeed(), vehicleInfo.speed ...});

input_feature.push_back(input);
static torch::jit::script::Module speedModel = torch::jit::load("PATH");

at::Tensor output_speed = speedModel.forward(input_feature).toTensor();
return output_speed.item<double>();
}
    void SimpleLaneChange::makeSignal(double interval) {

std::vector<torch::jit::IValue> input_feature;
auto input = torch::tensor ({vehicle->engine->getCurrentTime(),...});
input_feature.push_back(input);

static torch::jit::script::Module laneModel = torch::jit::load("PATH");
at::Tensor lane_choice = laneModel.forward(input_feature).toTensor();
signalSend->target = lane_choice;
}

    For large scale vehicle settings, you could also define control_config.xml and mapping the vehicle_id with assigned ML models intended to use.

    In next updated version, we will provide this detailed instructions for the defination of control_config.xml.

Release Plans

Release Checklist

  • Basic Platform Code
  • Integration with LibTorch
  • Functionality Release
    • Car Following Speed Model
    • Lane Changing Model
  • Install & Deployment guideline
  • Pre-trained model release

Featured Research and Projects Using CityFlow

Links