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Autonomous agents such as self-driving cars or parcel robots need torecognize and avoid possible collisions with obstacles in order to movesuccessfully in their environment. Humans, however, have learned to predictmovements intuitively and to avoid obstacles in a forward-looking way. The taskof collision avoidance can be divided into a global and a local level.Regarding the global level, we propose an approach called "Predictive CollisionManagement Path Planning" (PCMP). At the local level, solutions for collisionavoidance are used that prevent an inevitable collision. Therefore, the aim ofPCMP is to avoid unnecessary local collision scenarios using predictivecollision management. PCMP is a graph-based algorithm with a focus on the timedimension consisting of three parts: (1) movement prediction, (2) integrationof movement prediction into a time-dependent graph, and (3) time andrisk-dependent path planning. The algorithm combines the search for a shortestpath with the question: is the detour worth avoiding a possible collisionscenario? We evaluate the evasion behavior in different simulation scenariosand the results show that a risk-sensitive agent can avoid 47.3% of thecollision scenarios while making a detour of 1.3%. A risk-averse agent avoidsup to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent'sevasive behavior can be controlled actively and risk-dependent using PCMP.

Predictive Collision Management for Time and Risk Dependent Path Planning