Online and Predictive Warning System for Forced Lane Changes Using Risk Maps

Abstract

The survival analysis of driving trajectories allows for holistic evaluations of car-related risks caused by collisions or curvy roads. This analysis has advantages over common Time-To-X indicators, such as its predictive and probabilistic nature. However, so far, the theoretical risks have not been demonstrated in real-world environments. In this paper, we therefore present Risk Maps (RM) for online warning support in situations with forced lane changes, due to the end of roads.For this purpose, we first unify sensor data in a Relational Local Dynamic Map (R-LDM). RM is afterwards able to be run in real-time and efficiently probes a range of situations in order to determine risk-minimizing behaviors. Hereby, we focus on the improvement of uncertainty-awareness and transparency of the system. Risk, utility and comfort costs are included in a single formula and are intuitively visualized to the driver.In the conducted experiments, a low-cost sensor setup with a GNSS receiver for localization and multiple cameras for object detection are leveraged. The final system is successfully applied on two-lane roads and recommends lane change advices, which are separated in gap and no-gap indications. These results are promising and present an important step towards interpretable safety.

Importance Filtering with Risk Models for Complex Driving Situations

“…We add the driver errors in Risk Maps by also inputting the interface variables o per , v per , and p per from the last section. This is the main difference to the original Risk Maps behavior planner which was presented in [12]. On the bottom, Fig.…”

“…2 The risk model used for the perceived Risk Maps and the objective Risk Maps includes a Gaussian model that models 2D Gaussian distributions around the positions of the vehicles and estimates the collision risk between vehicles by taking the overlap of the Gaussians. For a description of the complete risk model, please refer to the work [12]. Fig.…”