Activation strategies and effectiveness of Intelligent safety systems for reducing pedestrian injuries in autonomous vehicles.

Intelligent safety systems (ISS) for autonomous vehicles, integrating advanced perception capabilities and passive protection devices, are expected to reshape traditional pedestrian safety systems and play a key role in reducing the risk of pedestrian injuries in traffic accidents. However, traditional active control and passive protection modules remain disconnected due to insufficient evidence supporting the effectiveness of collaborative strategies in integrated systems, particularly concerning activation criteria and timing. This study aims to address this gap by developing a comprehensive ISS that incorporates advanced perception systems, a vehicle dynamic control module, and controllable passive safety devices. Furthermore, the study evaluates the efficacy of trigger strategies in minimizing injury risks in various safety systems including Automatic Emergency Braking (AEB), Automatic Emergency Steering (AES), and ISS. To achieve this, we reconstructed the dynamics of pedestrian-vehicle interactions before collisions by examining 23 detailed collision cases. These cases were selected from real-world accident databases and included clear video recordings and detailed injury reports. Additionally, we analyzed the boundary conditions for collision avoidance by constructing vehicle steering and braking avoidance models. Our findings indicate that, in real-world accidents, the average Time-to-Collision (TTC) required for drivers to avoid collisions is -3.15 ± 1.00 s. In contrast, the AEB system requires -1.06 ± 0.23 s, and the AES system requires -0.44 ± 0.14 s. Building on this, we developed injury risk models for the system activation, predicting collision risks at various TTCs and pedestrian injury risks. The pedestrian injury risk prediction model effectively forecasts the risk of AIS3 + head injuries resulting from collisions between pedestrians aged 20 to 70 years and the vehicle hood. The threshold for a severe AIS3 + head injury risk is set at 10 %, with a trigger TTC of the ISS at -0.60 ± 0.20 s. When the system is activated at a TTC of -0.5 s, it can reduce the probability of severe head injury to pedestrians by 59 %. The design of the ISS shows significant potential for enhancing pedestrian safety. The findings of this research can offer guidance for the activation strategies of passive safety devices based on input signals from advanced perception systems in AVs.