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. As a result, they are only applicable in scenarios where those assumptions hold. To address this issue, we present a novel data-driven Probabilistic RISk Measure derivAtion (PRISMA) method. The PRISMA method is used to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). Because we adopt a data-driven approach to predict the possible future evolutions of traffic participant trajectories, less assumptions on these trajectories are needed. Since the PRISMA is not bound to specific assumptions, multiple SSMs for different types of scenarios can be derived. To calculate the probability of the specific event, the PRISMA method uses Monte Carlo simulations to estimate the occurrence probability of the specified event. We further introduce a statistical method that requires fewer simulations to estimate this probability. 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. 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：替代安全度量（SSMs）用于通过交通冲突中的安全风险来表达道路安全性。通常，SSMs依赖关于交通参与者轨迹未来演变的假设来生成风险度量，因此其仅适用于这些假设成立的情景。为解决此问题，我们提出了一种新颖的数据驱动型概率性风险度量推导（PRISMA）方法。PRISMA方法用于推导可实时计算特定事件（如碰撞）概率的SSMs。由于采用数据驱动方法预测交通参与者轨迹可能的未来演变，因此所需假设更少。由于PRISMA不受特定假设约束，可为不同类型场景推导多种SSMs。为计算特定事件的概率，PRISMA方法采用蒙特卡洛模拟估计该事件的发生概率。我们进一步引入了一种需要更少模拟次数即可估计该概率的统计方法，结合回归模型，使所推导的SSMs能够实现实时风险估计。为说明PRISMA方法，以纵向交通交互中的风险评估为例推导了一种SSM。客观比较两种SSMs的相对优劣极其困难甚至不可行，因此我们提供了一种基于期望风险趋势对所推导SSM进行基准测试的方法。基准测试应用表明，该SSM与期望风险趋势吻合。尽管所推导的SSM展示了PRISMA方法的潜力，未来工作将包括将该方法应用于其他类型的交通冲突，如侧向交通冲突或与弱势道路使用者的交互。