  Autonomous agents such as self-driving cars or parcel robots need to
recognize and avoid possible collisions with obstacles in order to move
successfully in their environment. Humans, however, have learned to predict
movements intuitively and to avoid obstacles in a forward-looking way. The task
of collision avoidance can be divided into a global and a local level.
Regarding the global level, we propose an approach called "Predictive Collision
Management Path Planning" (PCMP). At the local level, solutions for collision
avoidance are used that prevent an inevitable collision. Therefore, the aim of
PCMP is to avoid unnecessary local collision scenarios using predictive
collision management. PCMP is a graph-based algorithm with a focus on the time
dimension consisting of three parts: (1) movement prediction, (2) integration
of movement prediction into a time-dependent graph, and (3) time and
risk-dependent path planning. The algorithm combines the search for a shortest
path with the question: is the detour worth avoiding a possible collision
scenario? We evaluate the evasion behavior in different simulation scenarios
and the results show that a risk-sensitive agent can avoid 47.3% of the
collision scenarios while making a detour of 1.3%. A risk-averse agent avoids
up to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent's
evasive behavior can be controlled actively and risk-dependent using PCMP.
