A method for representing traffic conflict relationships based on target bearing angle: a vehicle–pedestrain case study

Traditional traffic conflict analysis typically relies on indicators such as time to collision (TTC) and post-encroachment time. However, while comparing these one-dimensional indicators and their derivatives can to some extent evaluate vehicle–pedestrian collision risk, they struggle to intuitively reflect the macroscopic interaction states between vehicles and pedestrians in a planar environment. Considering the polar-coordinate perception characteristics of light detection and ranging sensors (utilising angle and distance data), a traffic conflict relationship representation method based on measured target bearing angle (TBA) is proposed, which can directly represent traffic conflict relationships using polar-coordinate data without further calculating traditional conflict indicators such as TTC. By analysing TBA variation, it can directly assess the traffic conflict state. By employing graph envelope and regression methods, the state boundary of traffic interactions at different distances from the conflict point can be defined and expressed as a boundary function. On this basis, the approach can further support autonomous vehicles in evaluating the yielding necessity. Moreover, the study employs the random forest method to train an illustrative yielding speed model as an example, demonstrating the potential of the proposed methods to support yielding speed planning. The proposed approach may also provide insights for studies on restricting human-driven vehicles from forcing priority passage or delaying deceleration.