A data-driven spatio-temporal driving risk field mechanism for path planning

Abstract

Characterizing the future risk posed by surrounding human-driven vehicles is crucial for enhancing the safety of autonomous vehicles. Existing risk field methods build spatiotemporal risk fields using mathematical models with fixed parameters, making them struggle to capture dynamic human driving behaviors such as frequent acceleration, deceleration or lane changes, and are prone to overlooking rare but critical sudden events, which leads to unstable risk assessments in complex long-term scenarios. To address the aforementioned issues, a data-driven spatio-temporal risk field framework is proposed, which builds on a Bidirectional Deep Ultra-Gated Recurrent Unit (BDUGRU) to capture the high-dimensional spatio-temporal features of nearby vehicles and precisely predict vehicle distribution patterns over extended horizons. The introduced approach manages to yield a more accurate risk field and significantly improves long-term risk assessment in complex traffic environments. Furthermore, to validate the model’s practicality in engineering, we integrated Rapidly-exploring Random Tree with spatiotemporal data-driven risk field (SRF-RRT) and conducted path-planning simulations for autonomous vehicles using real-world traffic data. The results demonstrate that the proposed model excels in both prediction accuracy and reliability, and effectively reduces the measurement error based on collision time (TTC), offering strong applicability and providing a novel theoretical foundation and technological route for path planning in intelligent connected vehicles (ICVs).