Criticality metrics are not only essential for collision avoidance systems but also play a vital role for verification and validation of automated vehicles. With respect to the first application, criticality metrics should be real-time capable and applicable in various traffic situations. For the second application, holistic safety evaluation by criticality metrics is desired. However, existing criticality metrics hardly meet these two requirements. They are either only applicable in post-processing or only assess the safety of maneuvers in longitudinal direction. Therefore, we propose a new acceleration-based criticality metric, which is real-time capable and applicable in both longitudinal and lateral directions. The theory of the proposed criticality metric is introduced and the definition is explained according to different scenarios. A simulation platform is established to validate the criticality metric. The simulation results demonstrate that the proposed criticality metric takes all possible maneuvers into account when meeting a critical situation. Apart from the longitudinal behavior, the lateral behavior of automated vehicles can also be evaluated in real-time. Consequently, it has a wider application scope than other criticality metrics. To demonstrate its contribution to verification and validation of automated vehicles, we apply the criticality metric to a naturalistic driving dataset. The results prove that our criticality metric has a higher precision and recall than Time to Collision. Additionally, it combines the abilities of Time to Collision and Time Head Way to assess the safety of automated vehicles in the longitudinal direction. The proposed criticality metric is real-time capable and is suitable for different situations.

The choice of the reference point in automated vehicles impacts the vehicle's driving behavior. However, this influence is often not considered for planning and control tasks. To find out where the reference point should be located best, we first consider its position to be ideal if the needed lane width on the left and right side of the planned path is equal when cornering with constant curvature. For constantly curved paths we derive the ideal reference point depending on the curvature, using the kinematics of a slip angle free bicycle model. For non-stationary cornering, we analyze different maneuvers and finally, we select the reference point on the front axle. Utilizing this knowledge, the extent of a forward moving vehicle can be reduced to a point model, which does not require the orientation of the vehicle. This enables a simple and still promising approach for collision checking, where the vehicle's needed space is approximated by only one circle around the reference point. Finally, we analyze the influence of the reference point on a lateral feed-forward controller. Thus, we confirm the previously chosen reference point on the front axle for the equally distributed needed lane width and therefore recommend its use.

In the joint research project PRORETA 5, building blocks for automated driving in urban areas have been developed, implemented, and tested. The developed blocks involve an object tracking for cars, bicycles, and pedestrians that feeds a multimodal object prediction which is able to predict the traffic participants’ most likely trajectories. Then, an anytime tree-based planning algorithm calculates the vehicle’s desired path. Finally, logic-based safety functions ensure a collision-free trajectory for the ego vehicle. The mentioned building blocks were integrated and tested in a prototype vehicle in urban scenarios. Furthermore, a novel general framework for specifying and testing traffic rule compliance has been developed. In this paper, the automated driving concept of PRORETA 5 is introduced and the developed methods are briefly explained.