Name
Abstract | ||
|---|---|---|
This paper investigates the trajectory generation problem for an advanced driver assistance system that could sense the driving state of the vehicle, so that a collision free trajectory can be generated safely. Specifically, the problem of trajectory generation is solved for the safety assessment of the driving state and to manipulate the vehicle in order to avoid any possible collisions. The vehicle senses the environment so as to obtain information about other vehicles and static obstacles ahead. Vehicles may share the perception of the environment via an inter-vehicle communication system. The planning algorithm is based on a visibility graph. A lateral repulsive potential is applied to adaptively maintain a trade-off between the trajectory length and vehicle clearance, which is the greatest problem associated with visibility graphs. As opposed to adaptive roadmap approaches, the algorithm exploits the structured nature of the environment for construction of the roadmap. Furthermore, the mostly organized nature of traffic systems is exploited to obtain orientation invariance, which is another limitation of both visibility graphs and adaptive roadmaps. Simulation results show that the algorithm can successfully solve the problem for a variety of commonly found scenarios. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.3390/robotics2010019 | ROBOTICS |
Keywords | Field | DocType |
advanced driver assistance systems, trajectory generation, intelligent vehicles, path planning, visibility graphs | Motion planning,Computer vision,Visibility,Visibility graph,Invariant (physics),Advanced driver assistance systems,Communications system,Exploit,Control engineering,Artificial intelligence,Engineering,Trajectory | Journal |
Volume | Issue | Citations |
2 | 1 | 0 |
PageRank | References | Authors |
0.34 | 26 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher James Shackleton | 1 | 0 | 0.34 |
| Rahul Kala | 2 | 211 | 25.18 |
| Kevin Warwick | 3 | 129 | 21.37 |