In response to the decline in vehicle stability and the resulting safety risks
caused by inappropriate driver operations during high-speed emergency obstacle
avoidance, a human–machine cooperative control strategy based on driver
operation recognition is proposed. The strategy establishes a vehicle
controllability boundary by integrating real-time driver inputs with tire
adhesion limits, enabling dynamic evaluation of the influence of operations on
system controllability and identification of potential inappropriate operations.
On this basis, a control authority allocation mechanism is developed, capable of
adaptively adjusting to vehicle states and driver operations. By combining road
boundary constraints with vehicle stability envelope constraints, the strategy
dynamically regulates the steering angle, ensuring vehicle stability while
retaining the driver’s effective intentions as much as possible. Unlike
conventional path-tracking or single-envelope control approaches, the proposed
method achieves early identification and proactive mitigation of instability
risks induced by inappropriate driver operations, thereby reducing associated
safety hazards. To validate the effectiveness of the strategy, two
representative scenarios, double lane change and curve avoidance, were designed.
Simulation and driver-in-the-loop experiments demonstrate superior performance
in terms of vehicle stability, human–machine cooperation, and safety, achieving
a higher level of coordinated control and performance balance. The findings
provide new insights into the design of human–machine cooperative control
strategies under extreme conditions, contributing to enhanced fault tolerance of
intelligent driving systems against inappropriate driver operations and improved
driving safety.
