Beyond 1D and oversimplified kinematics: A generic analytical framework for surrogate safety measures

TL;DR

This paper introduces a versatile analytical framework for surrogate safety measures that accurately predicts vehicle trajectories and collision risks across various highway geometries and vehicle dynamics, validated through simulations and case studies.

Contribution

It develops a generic mathematical framework capable of handling multi-dimensional, high-fidelity vehicle models for real-time collision risk assessment in complex traffic environments.

Findings

The framework accurately predicts vehicle trajectories and time-to-collision.

Higher-dimensional SSMs significantly improve collision risk estimation accuracy.

Simulation results show 1D SSMs can be off by up to 300% compared to 3D models.

Abstract

This paper presents a generic analytical framework tailored for surrogate safety measures (SSMs) that is versatile across various highway geometries, capable of encompassing vehicle dynamics of differing dimensionality and fidelity, and suitable for dynamic, real-world environments. The framework incorporates a generic vehicle movement model, accommodating a spectrum of scenarios with varying degrees of complexity and dimensionality, facilitating the prediction of future vehicle trajectories. It establishes a generic mathematical criterion to denote potential collisions, characterized by the spatial overlap between a vehicle and any other entity. A collision risk is present if the collision criterion is met at any non-negative time point, with the minimum threshold representing the remaining time to collision. The framework's proficiency spans from conventional one-dimensional (1D) SSMs to extended multi-dimensional, high-fidelity SSMs. Its validity is corroborated through simulation experiments that assess the precision of the framework when linearization is performed on the vehicle movement model. The outcomes showcase remarkable accuracy in predicting vehicle trajectories and the time remaining before potential collisions occur. The necessity of higher-dimensional and higher-fidelity SSMs is highlighted through a comparison of conventional 1D SSMs and extended three-dimensional (3D) SSMs. The results showed that using 1D SSMs over 3D SSMs could be off by 300% for non-critical Time-to-Collision (TTC) values and about 20% for critical TTC values (below 1.5 seconds). Furthermore, the framework's practical application is demonstrated through a case study that actively evaluates all potential conflicts, underscoring its effectiveness in dynamic, real-world traffic situations.