Introducing Risk Shadowing For Decisive and Comfortable Behavior Planning
This addresses the challenge of efficient and safe behavior planning for self-driving cars in complex urban environments, though it is an incremental improvement over existing methods.
The paper tackles the problem of group interactions in urban driving by introducing risk shadowing, a method that analyzes interactions between three agents to identify which agents can be ignored in behavior planning, resulting in more decisive and comfortable driving strategies while ensuring safety.
We consider the problem of group interactions in urban driving. State-of-the-art behavior planners for self-driving cars mostly consider each single agent-to-agent interaction separately in a cost function in order to find an optimal behavior for the ego agent, such as not colliding with any of the other agents. In this paper, we develop risk shadowing, a situation understanding method that allows us to go beyond single interactions by analyzing group interactions between three agents. Concretely, the presented method can find out which first other agent does not need to be considered in the behavior planner of an ego agent, because this first other agent cannot reach the ego agent due to a second other agent obstructing its way. In experiments, we show that using risk shadowing as an upstream filter module for a behavior planner allows to plan more decisive and comfortable driving strategies than state of the art, given that safety is ensured in these cases. The usability of the approach is demonstrated for different intersection scenarios and longitudinal driving.