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About

Welcome to the official documentation of Python Library tactics2d!

tactics2d is an open-source Python library that provides diverse and challenging traffic scenarios for the development and evaluation of reinforcement learning-based decision-making models in autonomous driving. tactics2d stands out with the following key features:

  • Compatibility
  • 📦 Trajectory dataset -- Enables seamless importation of various real-world trajectory datasets, including Argoverse, Dragon Lake Parking (DLP), INTERACTION, LevelX Series (highD, inD, rounD, ExiD), NuPlan, and Waymo Open Motion Dataset (WOMD), encompassing both trajectory parsing and map information.
  • 📄 Map format -- Enables parsing and conversion of commonly used open map formats like OpenDRIVE, Lanelet2-style OpenStreetMap (OSM), and SUMO roadnet.
  • Customizability
  • 🚗 Traffic participants -- Supports the creation of new traffic participant classes with customizable physical attributes, physics dynamics/kinematics models, and behavior models.
  • 🚧 Road elements -- Support the definition of new road elements, with a focus on regulatory aspects.
  • Diversity
  • 🛣️ Traffic scenarios -- Features an extensive range of built-in Gym-style traffic scenarios, including highway, lane-merging, unsignalized/signalized intersection, roundabout, parking, and racing.
  • 🚲 Traffic participants -- Features a variety of built-in traffic participants with realistic physics parameters, detailed further here.
  • 📷 Sensors -- Provides bird-eye-view (BEV) semantic segmentation RGB image and single-line LiDAR point cloud for model input.
  • Visualization -- Offers a user-friendly visualization tool for real-time rendering of traffic scenarios and participants, along with the capability to record and replay traffic scenarios.
  • Reliability -- Over 85\% code is covered by unit tests and integration tests.

What can you do with tactics2d?

[Description]

Features

Updated on April 1, 2024.

Corresponds to version 0.1.6.

Dataset Parser

Support parsing maps and trajectories from the following datasets:

  • HighD
  • InD
  • RounD
  • ExiD
  • Argoverse
  • Dragon Lake Parking (DLP)
  • INTERACTION
  • NuPlan
  • WOMD

Map Parser

Support parsing maps in the following formats:

  • OpenStreetMap (OSM)
  • OpenStreetMap annotated in Lanelet2
  • OpenDRIVE (XODR)

Math Interpolation Algorithms

Support the following interpolation algorithms:

  • B-Spline
  • Bezier
  • Cubic
  • Spiral
  • Dubins
  • Reeds Shepp

Traffic Participant

The following traffic participants are implemented:

  • Vehicle
  • Cyclist
  • Pedestrian

For each traffic participants, a set of parameters are available to configure the behavior.

Physics Model

The following physics model of traffic participants are supported:

  • Bicycle model (Kinematic): recommended for cyclists and low-speed vehicles
  • Bicycle model (Dynamic): recommended for cyclists and high-speed vehicles
  • Point mass (Kinematic): recommended for pedestrians
  • Single-track drift model (Dynamic): recommended for vehicles

Road Element

The following road elements are implemented:

  • Lane
  • Area
  • Junction
  • Road line
  • Base class of traffic regulations

Traffic Event Detection

  • Static collision detection
  • Dynamic collision detection
  • Arrival event detection

Sensor

  • Bird-eye-view (BEV) semantic segmentation RGB image
  • Single-line LiDAR point cloud

Why tactics2d?

Similar Works

tactics2d is crafted to offer a robust and intuitive environment tailored for the development and evaluation of autonomous driving decision-making models. As a third-party library, tactics2d does not cater to any specific dataset; instead, its focus lies in facilitating parsing, visualization, log replaying, and interactive simulation across a diverse array of datasets and map formats. The table below provides a comparative overview of tactics2d alongside other open-source simulators under active maintenance.

These tables are updated on 2024-04-01. Notations: ✅ = Implemented and tested 🚧 = Under development - = Not implemented and not planned

Functionality

Simulator Built-in RL Environment Custom Trajectory Custom Map Dataset Compatibility Interactive NPCs Multi-agent
SUMO - ✅ ✅ - ✅ -
CarRacing ✅ - - - - -
CARLA - ✅ ✅ - ✅ ✅
CommonRoad ✅ ✅ ✅ ✅ ✅ -
highway-env ✅ - - - ✅ -
SMARTS ✅ - - ✅ ✅ ✅
MetaDrive ✅ ✅ ✅ - ✅ ✅
NuPlan - - - - ✅ -
InterSim - - - ✅ ✅ ✅
TBSim - - - ✅ ✅ ✅
Waymax ✅ - - - ✅ ✅
Tactics2D ✅ ✅ ✅ ✅ 🚧 🚧

Dataset Compatibility

tactics2d excels in parsing various datasets into unified map and traffic participant data structures, enabling seamless integration for both log replay and interactive simulations.

Below is a comparison of dataset support between tactics2d and other simulators. tactics2d strives to accommodate a wide range of datasets, and we commit to keeping the table updated on a regular basis.

TODO

We have a plan to add support to the following datasets in the future:

  • NGSIM
  • Lyft 5

Feel free to suggest additional trajectory datasets to be incorporated into our support list by either opening an issue or submitting a pull request. We value community input and are committed to expanding our dataset coverage to better serve our users.

Simulators Argoverse DLP INTERACTION LevelX NuPlan WOMD
SUMO - - - - - -
CarRacing - - - - - -
CARLA - - - - - -
CommonRoad - - ✅ ✅ - -
highway-env - - - - - -
SMARTS - - - - - -
MetaDrive - - - - - -
NuPlan - - - - ✅ -
InterSim - - - - ✅ ✅
TBSim - - - - ✅ ✅
Waymax - - - - - ✅
Tactics2D ✅ ✅ ✅ ✅ ✅ ✅

Map Format Compatibility

Simulators OpenDRIVE OpenStreetMap SUMO Roadnet
SUMO ✅ ✅ ✅
CarRacing - - -
CARLA ✅ ✅ ✅
CommonRoad ✅ ✅ -
highway-env - - -
SMARTS ✅ - ✅
MetaDrive ✅ - ✅
NuPlan - - -
InterSim - - -
TBSim - - -
Waymax - - -
Tactics2D ✅ ✅ 🚧