Balancing risk against utility: Behavior planning using predictive risk maps

This paper addresses the problem of future behavior evaluation and planning for ADAS in general traffic situations. Complex traffic situations require the estimation of future behavior alternatives in terms of predictive risks. Based on the predicted future dynamics of traffic scene entities, we present an approach where a continuous, probabilistic model for future risks is used to build so-called predictive risk maps. These maps indicate how risky a certain ego-car trajectory will be at different predicted times so that they can be used to directly plan the best possible future behavior. Since this optimization problem is highly non-convex we combine the risk maps with sampling-based planning algorithms of the RRT*-type to obtain future trajectories which minimize risk and maximize utility. We apply our approach to multiple risk types and various different scenarios, including inner city and highway situations.