A Human-Like Free-Lane-Change Trajectory Planning and Control Method With Data-Based Behavior Decision

Free lane change (FLC) is an important research direction of intelligent driving vehicles. In this paper, a lane change decision model based on deep learning is established with ego and adjacent lane risk and properties of environmental vehicles. A long short-term memory neural network that can be associated with time series characteristics is used to model the lane change process based on analysis of factors affecting the lane change decision. The result of decision-making model training shows that the recognition accuracy of lane change and lane keeping decision reaches more than 92%. Then, a human-like FLC trajectory is planned based on polynomial curve, a quintic polynomial trajectory cluster based on preview distance is generated according to driving conditions, and the optimal FLC trajectory is selected by optimizing an objective function. The model predictive control method is used to dynamically control the vehicle to follow the trajectory. Finally, the lane change decision, trajectory planning, and tracking control model are built in simulation environment, and the control of vehicle dynamics model verifies the integrity and feasibility of free lane change function.