The invention is suitable for the technical field of automatic driving, and provides a dynamic Riemannian manifold artificial
potential field obstacle avoidance method based on PPO
reinforcement learning, and the method comprises the steps: dividing obstacles into three classes: pedestrians, vehicles and static obstacles through a target detection
algorithm, and determining the
risk distribution characteristics of each class; secondly, a 11-dimensional high-dimensional space-time
state space is constructed, and risk pre-judgment is achieved; a PPO
reinforcement learning algorithm is introduced, a high-dimensional state is used as input, four-dimensional
potential field deformation parameters for three types of obstacles are output, and a differentiated continuous
potential field form is autonomously learned; constructing a
covariance matrix based on Riemannian geometry, generating a non-abrupt-change anisotropic repulsion field, and superposing a virtual escape force to crack a local minimum value; and synthesizing the
gravitational force, the total repulsive force and the escape force to obtain a total potential field force, and outputting a
steering angle and acceleration instruction conforming to
vehicle dynamics constraints. According to the strategy, a complete
obstacle avoidance framework is constructed, and the technical limitation of a traditional scheme is effectively relieved.