An integrated potential safety hazard assessment framework in connected car-following scenario

In continuous car-following scenarios, a minor conflict could be amplified along the platoon over time due to system instability, resulting in high-risk rear-end situations. Conventional surrogate safety measures (SSM) only adopt the motion information of ego vehicle and preceding one, failing to capture potential safety hazard beyond the leading vehicle in the platoon. Identifying potential safety hazard beyond the 1st preceding one is substantially important for connected and automated vehicles (CAVs) which could take proactive actions to prevent high-risk scenarios. Toward this end, drawing on reliability theory, the current paper developed an integrated potential safety hazard assessment framework considering the motion information of preceding multiple vehicles. An innovative surrogate safety measures (SSM), named the potential safety hazard index (PSHI), is developed to capture potential safety hazard from preceding multiple vehicles in the car-following environment. We tested the validity of proposed PSHI in a real-world rear-end crash scenario. To real-timely apply PSHI to CAVs, we developed an orthogonal transformation first order reliability method (OTFORM) to accelerate computational time, keeping computation burden within 0.01 s. Another worthwhile finding is potential safety hazard mainly comes from preceding three vehicles in risky car-following cases. The new potential safety hazard evaluation framework provides a creative perspective for safety assessment and also shows a good prospect for longitudinal safety control of CAVs strategies.