Name
Title | ||
|---|---|---|
Data-Driven State-Increment Statistical Model and Its Application in Autonomous Driving. |
Abstract | ||
|---|---|---|
The aim of trajectory planning is to generate a feasible, collision-free trajectory to guide an autonomous vehicle from the initial state to the goal state safely. However, it is difficult to guarantee that the trajectory is feasible for the vehicle and the real path of the vehicle is collision-free when the vehicle follows the trajectory. In this paper, a state-increment statistical model (SISM) ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TITS.2018.2797308 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Trajectory,Actuators,Kinematics,Mathematical model,Autonomous vehicles,Predictive models | Control theory,Data-driven,Kinematics,Curvature,Control theory,Simulation,Gaussian,Statistical model,Engineering,Trajectory,Actuator | Journal |
Volume | Issue | ISSN |
19 | 12 | 1524-9050 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Ma | 1 | 85 | 27.49 |
| J. Xue | 2 | 542 | 57.57 |
| Yuehu Liu | 3 | 181 | 41.53 |
| Jing Yang | 4 | 158 | 58.81 |
| Yongqiang Li | 5 | 310 | 35.01 |
| Nanning Zheng | 6 | 3975 | 329.18 |