Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps
This work addresses safety for autonomous or assisted driving systems in occlusion-prone traffic scenarios, but it is incremental as it builds on existing local dynamic map and survival analysis methods.
The paper tackles the problem of evaluating collision risks at intersections with limited visibility by modeling occluded entities and predicting worst-case trajectories for risk estimation, validating the system on real-world scenarios where it mimics human driver behavior.
This work addresses the task of risk evaluation in traffic scenarios with limited observability due to restricted sensorial coverage. Here, we concentrate on intersection scenarios that are difficult to access visually. To identify the area of sight, we employ ray casting on a local dynamic map providing geometrical information and road infrastructure. Based on the area with reduced visibility, we first model scene entities that pose a potential risk without being visually perceivable yet. Then, we predict a worst-case trajectory in the survival analysis for collision risk estimation. Resulting risk indicators are utilized to evaluate the driver's current behavior, to warn the driver in critical situations, to give suggestions on how to act safely or to plan safe trajectories. We validate our approach by applying the resulting intersection warning system on real world scenarios. The proposed system's behavior reveals to mimic the general behavior of a correctly acting human driver.