Study On Active Collision Avoidance Trajectory Optimization And Adaptive Collaborative Control For Intelligent Vehicles

Thanks to the development of computer technology,sensing technology and automatic control technology,intelligent vehicles are rapidly moving from Level 2autonomous driving to higher-level autonomous driving technologies,and eventually achieve fully autonomous driving.However,despite this,intelligent driving still faces many technical challenges.For example,in highly dynamic environments,there exist complicated and changeable traffic scenes and unexpected situations,which make it difficult for automated vehicles to complete self-driving tasks.Aiming at the above problems,this paper takes four-wheel-drive smart cars as the research object,and designs the trajectory planning and motion control strategies for intelligent vehicles traveling on structured roads.The full text of the research is shown as follows:1)It is difficult for curve interpolation based path planning algorithms to balance the path quality and computing cost due to its simple planning strategies.In this paper,in order to avoid this issue,by using some clever geometric properties of the Bézier curve in combination with improved risk potential field,smooth and continues paths are real-time generated.Furthermore,this method can be widely applied in various traffic situations.2)The applicable bound of the improved risk potential field has been further expanded to make it be suitable for more complex and changeable traffic environments.Moreover,by setting an appropriate cost function,the driving safety,comfortability and driving efficiency of the sampled candidate paths can be evaluated together.In addition,the conversion relationship between the traditional Frenet coordinate system and the Cartesian coordinate system is much complicated and computationally inconvenient.This paper proposes a simpler direct mapping method,which reduces the complexity of traditional discrete optimization algorithms.3)In order to make the intelligent to follow the generate trajectory precisely,this paper presents a lateral and longitudinal coordinated control model by combing the vehicle dynamic model with the visual preview model.A lateral sliding mode controller based on adaptive preview distance is designed,and furthermore,the PSO(Particle Swarm Optimization)algorithm is employed to balance the trajectory tracking accuracy,the steering portability and the vehicle stability.Besides,the longitudinal controller is designed based on acceleration preview model and the vehicle longitudinal dynamic model,which realizes the coordinated control for intelligent vehicle.4)In order to verify the validity of the trajectory planning and motion control model proposed above,a joint simulation platform based on Carsim and MATLAB /Simulink software has been set up.Simulation results demonstrate that the intelligent vehicle trajectory planning and motion control algorithm proposed in this paper can plan an obstacle avoidance trajectory in real time,and guide the vehicle to travel on the generated trajectory in consideration of driving safety,comfortability,efficiency and vehicle stability.