Spatio-temporal Semantic Corridor

0. News

21 Sept. 2020: The whole dependencies and a playable demo can be found in: https://github.com/HKUST-Aerial-Robotics/EPSILON

31 August 2019: The code for the ssc planner is available online!

3 July 2019: Our paper is available online!

• Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor, Wenchao Ding, Lu Zhang, Jing Chen and Shaojie Shen IEEE Xplore. W. Ding and L. Zhang contributed equally to this project.

  @article{ding2019safe,
  title={Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor},
  author={Ding, Wenchao and Zhang, Lu and Chen, Jing and Shen, Shaojie},
  journal={IEEE Robotics and Automation Letters},
  year={2019},
  publisher={IEEE}
  }

What Is Next: The code for the dependencies of this planner is comming soon!

1. Introduction

This is the project page for the paper ''Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor'' which is published at IEEE Robotics and Automation Letters (RA-L).

This project contains (already released):

• ssc_map: maintainer for the semantic elements in the spatio-temporal domain.
• ssc_planner: planner for generating the semantic corridor in the spatio-temporal domain and optimizing safe and dynamically feasible trajectories.
• ssc_server_ros: ros server which manages the replanning.
• ssc_visualizer: visualizing the elements both in the spatio-temporal domain (in a separate rviz window) and in the global coordinate.

The dependencies of this project includes (comming soon):

• common package: an integration of various mathematical tools such as polynomial, spline, primitive, lane, trajectory, state, optimization solvers, etc. It provides many easy-to-use interfaces for mathematical modeling.
• phy_simulator package: a configurable multi-agent simulator. It provides ground truth information and listens planner feedbacks.
• semantic_map_manager package: map with semantic information for vehicle local planning. Each agent is capable of rendering its local planning map based on its configuration.
• vehicle_model package: basic vehicle models and controllers.
• vehicle_msgs package: ros communication messages and corresponding encoder and decoders.
• playgrounds package: test cases/configurations/scenarios stored in json format.
• behavior_planner package: mpdm behavior planner for on-road driving. It can provide a local reference lane based on navigation information and behavior decision.
• forward_simulator package: forward simulation
• motion_predictor package: surrounding vehicle motion prediction.
• route_planner package: road-level route planner, a simple version.

The dependencies will be released in another repo: https://github.com/HKUST-Aerial-Robotics/HDJI_planning_core.

The overall structure is as follows:

Videos:

2. Prerequisites

3. Build

4. Usage

5. Demos