DocumentCode :
1894725
Title :
Task planning for highly automated driving
Author :
Chao Chen ; Gaschler, Andre ; Rickert, Markus ; Knoll, Alois
Author_Institution :
An-Inst., Tech. Univ. Munchen, Munich, Germany
fYear :
2015
fDate :
June 28 2015-July 1 2015
Firstpage :
940
Lastpage :
945
Abstract :
A hybrid planning approach is presented in this paper with the focus of integrating task planning and motion planning for highly automated driving. In the context of task planning, the vehicle and environment states are transformed from the continuous configuration space to a discrete state space. A planning problem is solved by a search algorithm for an optimal task sequence to reach the goal conditions in the symbolic space, regarding constraints such as space topology, place occupation, and traffic rules. Each task can be mapped to a specific driving maneuver and solved with a dedicated motion planning method in the continuous configuration space. The task planning approach not only bridges the gap between high-level navigation and low-level motion planning, but also provides a modular domain description that can be developed and verified individually. Our task planner for automated driving is evaluated in several scenarios with prior knowledge about the road-map and sensing range of the vehicle. Behavior that is otherwise complex to achieve is planned according to traffic rules and re-planned regarding the on-line perception.
Keywords :
mobile robots; path planning; road traffic; state-space methods; topology; continuous configuration space; discrete state space; driving maneuver; high-level navigation; highly automated driving; hybrid planning approach; low-level motion planning; modular domain description; motion planning method; online perception; optimal task sequence; place occupation; road-map; search algorithm; sensing range; space topology; symbolic space; task planning approach; traffic rule; Junctions; Planning; Robots; Sensors; Space vehicles; Switches;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2015 IEEE
Conference_Location :
Seoul
Type :
conf
DOI :
10.1109/IVS.2015.7225805
Filename :
7225805
Link To Document :
بازگشت