Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk, restricting their applicability to scenarios where these assumptions are valid. In response to this limitation, we present the novel Probabilistic RISk Measure derivAtion (PRISMA) method. The objective of the PRISMA method is to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). The PRISMA method adopts a data-driven approach to predict the possible future traffic participant trajectories, thereby reducing the reliance on specific assumptions regarding these trajectories. Since the PRISMA is not bound to specific assumptions, the PRISMA method offers the ability to derive multiple SSMs for various scenarios. The occurrence probability of the specified event is based on simulations and combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. Since there is no known method to objectively estimate risk from first principles, i.e., there is no known risk ground truth, it is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users.

翻译：代理安全度量（SSMs）用于以交通冲突中的安全风险表达道路安全。通常，SSMs依赖于对交通参与者轨迹未来演变的假设来生成风险度量，这限制了其在假设有效场景中的适用性。针对这一局限，我们提出了新颖的概率性风险度量推导方法（PRISMA）。PRISMA方法的目标是导出可实时计算特定事件（如碰撞）发生概率的SSMs。该方法采用数据驱动方式预测交通参与者未来可能的轨迹，从而减少对此类轨迹特定假设的依赖。由于PRISMA不受限于特定假设，因此能够为多种场景导出多个SSMs。指定事件的发生概率基于仿真计算，并结合回归模型，使导出的SSMs能进行实时风险估计。为阐述PRISMA方法，我们针对纵向交通交互中的风险评估导出了一个SSM。由于尚无客观评估风险的第一性原理方法（即不存在已知的风险真值），客观比较两个SSMs的相对优劣极其困难甚至不可能。因此，我们提供了一种基于预期风险趋势对导出SSM进行基准测试的方法。基准测试应用表明，该SSM与预期风险趋势吻合。尽管导出的SSM展示了PRISMA方法的潜力，未来工作将涉及将该方法应用于其他类型的交通冲突，如横向交通冲突或与弱势道路使用者的交互。