In this article, an active collision avoidance based on improved artificial potential field is proposed to satisfy collision avoidance for intelligent vehicle. A longitudinal safety distance model based on analysis of braking process and a lane-changing safety spacing model based on minimum time of lane changing under the constraint of sideslip angle are presented. In addition, an improved artificial potential field method is introduced, which represents the influence of environmental information with artificial force. Simulation results demonstrate the superior performance of the proposed algorithm over collision avoidance for intelligent vehicle.

This paper provides a multi-agent coordinated control system to improve the real-time performance of intelligent vehicle active collision avoidance. At first, the functions and characteristics of longitudinal and lateral collision avoidance agents are analyzed, which are the main components of the multi-agent. Then, a coordinated solution mechanism of an intelligent vehicle collision avoidance system is established based on hierarchical control and blackboard model methods to provide a reasonable way to avoid collision in complex situations. The multi-agent coordinated control system can handle the conflict between the decisions of different agents according to the rules. Comparing with existing control strategies, the proposed system can realize multi decisions and planning at the same time; thus, it will reduce the operation time lag during active collision avoidance. Additionally, fuzzy sliding mode control theory is introduced to guarantee accurate path tracking in lateral collision avoidance. Finally, co-simulation of Carsim and Simulink are taken, and the results show that the real-time behavior of intelligent vehicle collision avoidance can be improved by 25% through the system proposed.

Ant colony algorithm or artificial potential field is commonly used for path planning of autonomous vehicle. However, vehicle dynamics and road adhesion coefficient are not taken into consideration. In addition, ant colony algorithm has blindness/randomness due to low pheromone concentration at initial stage of obstacle avoidance path searching progress. In this article, a new fusion algorithm combining ant colony algorithm and improved potential field is introduced making autonomous vehicle avoid obstacle and drive more safely. Controller of path planning is modeled and analyzed based on simulation of CarSim and Simulink. Simulation results show that fusion algorithm reduces blindness at initial stage of obstacle avoidance path searching progress and verifies validity and efficiency of path planning. Moreover, all parameters of vehicle are changed within a reasonable range to meet requirements of steering stability and driving safely during path planning progress.

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Drivers of man-machine cooperative driving intelligent vehicles are affected by driving skills, physiological reactions, and other factors. Under emergency conditions, they often subconsciously forcefully take over control rights and produce unreasonable stress steering, which brings new accident risks to vehicles. To avoid collisions, this paper proposes an emergency collision avoidance control strategy for man-machine cooperative driving vehicles. In the collision avoidance path planning layer, considering the obstacle distance, road adhesion coefficient, vehicle speed, steering wheel stress angle, and driver's linear steering cognition, a circular arc lane-change path is designed. The curvature mutation is smoothed using the third-order Bezier function. In the tracking control layer, a method of additional yaw moment control is designed by using the model predictive control (MPC) algorithm to track the path. The accuracy and safety of vehicle tracking are guaranteed only by adjusting the braking torque of each wheel of the vehicle, to correct the unreasonable input when the driver forces to take over. The co-simulation results show that the collision avoidance control system can effectively correct the unreasonable input during forced take-over, and ensure the safety of stress steering.INDEX TERMS Men-machine cooperative driving vehicles, additional yaw moment control, emergency collision avoidance system, model predictive control.