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.

翻译：驾驶轨迹的生存分析能够全面评估由碰撞或弯道引起的车辆相关风险。与常见的时间至碰撞（Time-To-X）指标相比，该方法具有预测性和概率性等优势。然而，迄今为止，理论风险尚未在真实环境中得到验证。为此，本文针对因道路尽头导致的强制变道场景，提出了基于风险地图（RM）的在线预警支持方案。首先，我们将传感器数据统一整合至关系型局部动态地图（R-LDM）中。随后，RM可实现实时运行，并通过高效探测多种场景来确定风险最小化的行为。在此过程中，我们重点提升了系统的不确定性感知能力与可解释性。风险、效用与舒适度成本被统一纳入单一公式，并以直观方式向驾驶员可视化呈现。在实验环节，我们采用了低成本传感器配置，包括用于定位的GNSS接收器和用于目标检测的多摄像头系统。该最终系统成功应用于双车道道路，可提供变道建议，并将其区分为可切入间隙与无间隙场景。这些结果具有良好前景，标志着向可解释安全性迈出了重要一步。