EXTENSION-DECISION-BASED ADAPTIVE COLLISION AVOIDANCE CONTROL FOR VEHICLES

Vehicle collision avoidance control can effectively avoid or mitigate vehicle collision accidents, which is one of the key control technologies of autonomous vehicles. Various traffic conditions, uncertain road adhesion coefficient and complex hydraulic brake execution system will reduce the effectiveness of collision avoidance control. Therefore, an adaptive collision avoidance control (ACAC) based on extension decision method is proposed in this paper. The control method is adaptive to the road adhesion coefficient and can accurately control the brake fluid pressure of the braking system. Firstly, the tire longitudinal force is estimated by the sliding mode observer (SMO), The designed SMO uses only the signal measured by sensors on board, i. e. the wheel angular speed to estimate the longitudinal tire force. Based on the observed tire longitudinal force, the road adhesion coefficient is estimated by the recursive least square method with forgetting factor (FFRL). Secondly, based on the estimated value of the road adhesion coefficient by FFRL, an ACAC method based on adaptive adjustment of road adhesion coefficient is proposed. This method determines which collision avoidance control strategy is adopted, that is, the extension decision method is used to judge whether short point braking, full braking or no braking. The impending collision time is defined as the main characteristic quantity, and the collision danger distance is the auxiliary characteristic quantity. A two-dimensional extension set is established based on the motion relationship between the ego vehicle and preceding vehicle and it can be divided into three different domains: the classical domain, the extension domain, and the non-domain. Thirdly, the effect of active deceleration control (ADC) is realized through precise hydraulic control by the executive system of electronic hydraulic braking system. Finally, the above methods are verified by manner of software joint simulation. The results show that the proposed algorithm has good collision avoidance effect in collision avoidance driving conditions.