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 simulationmotion_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: