Driver-Oriented Active Intervention Control Framework for Human–Machine Shared Collision Avoidance

In formulating a driver-oriented human-machine shared control (HMSC) system, an intelligent driving system (IDS) should ideally minimize interventions under normal driving conditions while providing timely and necessary interventions in hazardous situations. To achieve this balance, this paper proposes a novel driver-oriented active intervention control (DAIC) framework for automotive collision avoidance (CA). As a foundation, a spatiotemporal collision risk evaluation (SCRE) method is developed by integrating collision probability (CP) and time-to-collision (TTC) metrics, comprehensively quantifying collision risks in both spatial and temporal dimensions. Subsequently, a short-horizon driver behavior prediction network (SDBP-Net) based on bidirectional long short-term memory (BiLSTM) is proposed, enabling effective prediction of short-horizon driver input increment sequence and corresponding vehicle trajectories. Leveraging these developments, a partially cooperative game-based HMSC strategy is formulated to dynamically allocate control authority between the driver and IDS. Driver-in-the-loop (DIL) experiments validate the effectiveness of the proposed DAIC framework. The results demonstrate that the DAIC framework significantly reduces unnecessary IDS interventions while maintaining collision safety, effectively balancing driving safety and driver acceptance. This work highlights the practical potential of the feedforward HMSC system in achieving safer, more intuitive and driver-friendly collision avoidance assistance.