Data-Driven Risk Assessment with Scenario Classification for Collision Avoidance in Left/Right Turn Across Path Conflicts

Traffic safety is one of the vital issues in developing autonomous and assisted driving systems. To achieve higher level of driving automation, it becomes necessary to have a reliable Risk Assessment (RA) method that not only responds to current traffic conditions but also anticipates future scenario propagation. Regardless of the driver intentions, traditional deterministic risk indicators like Time-To-Collision (TTC) have proven effective but fall short in addressing the inherent uncertainty in future propagation, especially under conflict scenarios influenced by interdependent decisions of traffic participants. Acceleration for Collision Avoidance (ACA) emerges as a flexible risk criterion adaptable to different collision-avoidance maneuvers. Focusing on the Left/Right Turn across path conflicts, this work aims to propose an innovative surrogate risk indicator for collision avoidance with the combination of ACA criteria and scenario classification using Hidden Markov Models (HMM). Based on a near-miss video database, we further train and evaluate the presented model, supplying interpretability and adaptability of risk assessment in complex conflict scenarios.