Omg Tools
Optimal Motion Generation-tools: motion planning made easy
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Path_planner | 1,187 | 8 months ago | 14 | bsd-3-clause | C++ | |||||
| Hybrid A* Path Planner for the KTH Research Concept Vehicle | ||||||||||
| Omg Tools | 242 | 3 years ago | 4 | December 01, 2017 | 26 | lgpl-3.0 | Python | |||
| Optimal Motion Generation-tools: motion planning made easy | ||||||||||
| Hybrid A Star Annotation | 62 | 4 years ago | 2 | bsd-3-clause | C++ | |||||
| Hybrid A*路径规划器的代码注释 | ||||||||||
| Motionplanner | 49 | 5 years ago | 3 | Python | ||||||
| Motion Planner for Self Driving Cars | ||||||||||
| Efficient Motion Planning | 17 | 2 years ago | 2 | MATLAB | ||||||
| To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles | ||||||||||
| Motion Planning Based On Model Predictive Control And Bezier Spline | 14 | 5 years ago | Jupyter Notebook | |||||||
| A quasi Hybrid A* method is introduced for motion planning of autonomous driving car, based on MPC and Bezier spline | ||||||||||
| Cooperative Lane Changes Of Cavs | 7 | 5 years ago | gpl-3.0 | Matlab | ||||||
| Codes for "Balancing Computation Speed and Quality: A Decentralized Motion Planning Method for Cooperative Lane Changes of Connected and Automated Vehicles" | ||||||||||
| Cooperative Lane Changes Trajectory Planning Of Cavs | 6 | 5 years ago | gpl-3.0 | AMPL | ||||||
| Codes of "Cooperative Lane Change Motion Planning of Connected and Automated Vehicles: A Stepwise Computational Framework" | ||||||||||
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Python
Vehicle
Motion Planning