Risk mitigation is an important element to consider in risk evaluation. Safety features have helped to decrease the death ratio over the years. However, to date, each driver assistance system works on a single domain of operation. The problem remains in how to use perception to contextualize the scene to fully minimize the collision severity in a complex emergency scenario. Up to now, works on cost maps have consider simple contextualized object in mitigation scenarios. For instance, the use of binary allowed/forbidden zones or, a fixed weight to each type of object in the scene. Our work employs the risk of injury issued by accidentology to each class of object present in the scene. Each class of object presents an injury probability with respect to the impact speed and ethical/economical/political factors. The method generates a cost map containing a collision probability along with to the risk of injury. It dynamically contextualizes the objects, since the risk of injury depends on the characteristics of the scene. Simulation and dataset results validate that changing the referred parameters alters the context and evaluation of the scene. Then, the proposed method allows a better assessment of the surroundings by creating a dynamic navigation cost map for complex scenarios.

Collision mitigation is an important element in motion planning. Although Advanced Driver-Assistance Systems (ADAS) have a rich number of functionalities, they lack interchangeability. There is still a gap on finding a way to evaluate the best decision globally. This paper presents a novel motion planning framework to generate emergency maneuvers in complex and risky scenarios using active mitigation. The classical Model Predictive Path Integral (MPPI) algorithm is improved to be used in a probabilistic dynamic cost map under limited perception range. A cost map with global probability of injury to all road users is used as a constraint to the problem in order to compute target selection based on the global minimum risk considering all road users. Real experiments introduce the use of augmented sensor data by merging simulation and real sensor data to safely produce collision and mitigation experiments. Results show that the proposed algorithm can perform correctly in real time on board of the vehicle, by finding collision-free trajectories in complex scenarios and compute viable target selection that minimizes global injury risk when collision is inevitable.