Robust control of lateral obstacle avoidance for intelligent vehicle shared-driven by people and vehicles based on dynamic early warning

Abstract

In the paper, a novel framework for the control system of lateral obstacle avoidance (LOA), which is based on dynamic early warning for intelligent vehicles shared-driven by people and vehicles (IVSDPVs), is presented to perform LOA in dynamic conditions (e.g. the speed of obstacle vehicles changes). Firstly, to achieve the accurate warning of IVSDPVs and adjust that with dynamic intervention of the driver in dynamic conditions, a multi-level early warning algorithm based on fusion and complementarity of the critical safe distance and reciprocal of collision time is proposed. Moreover, the critical safe distance is obtained using a combination of longitudinal with lateral directions. Secondly, if the driver does not respond until the “Critical” level warning, the IVSDPVs is automatically taken over by the control strategy to conduct active LOA control. Thirdly, to overcome the parameters perturbation, that is, mass, cornering stiffness, and signal disturb, the robust control strategy is presented to achieve accurate trajectory tracking of LOA. Finally, the effectiveness of the proposed control strategy is evaluated by testing on the Simulink/Trucksim platform to demonstrate the controller’s capability in obtaining accurate early warning and achieving LOA across various working conditions.

A road adhesion coefficient-tire cornering stiffness normalization method combining a fractional-order multi-variable gray model with a LSTM network and vehicle direct yaw-moment robust control

“…Assuming that their values are represented within certain, known intervals, and v x is constant in steering process. m and I z can be described as upper linear fractional transformations (ULFT; Lian et al, 2018 ; Nie et al, 2023 ), respectively.…”