Improved Collision Risk Assessment for Autonomous Vehicles at on-Ramp Merging Areas

Unsafe lane-changing maneuvers contribute to accidents and merging conflicts due to variations in traffic states and driver behaviors at on-ramp merging areas. Connected and autonomous vehicle (CAV) technologies promise significant improvements to traffic management systems, including the possibility of reducing collisions. CAVs provide various driving supports, which are expected to reduce collisions by exploiting information from surrounding vehicles. This paper proposes a collision avoidance (CA) model that predicts the occurrence of collision events associated with different vehicle movements in merging areas. A decision-making system consisting of a threat assessment model is proposed to assess the risks associated with different movements, and to avoid collisions based on safe lateral and longitudinal acceleration of the on-ramp vehicle in the merging area. Then, evasive action of the main lane vehicle is assessed based on its braking response during the merging interaction with the on-ramp vehicle. Finally, in emergency situations, a vehicle stabilization mechanism is introduced to preserve the vehicle states within the envelope of danger. The results show that the model could be used to avoid collisions in multiple scenarios and predict the occurrence of collision events associated with different vehicle movements. Moreover, we demonstrate the effectiveness of the proposed model using the Next Generation Simulation (NGSIM) I-80 trajectory dataset. The findings show that the proposed model can be useful for avoiding collisions in real-time scenarios. In summary, the proposed CA model provides a valuable safety management tool.