Unmanned vehicle lane changing decision-making method and system based on adversarial imitation learning

Abstract

The invention discloses an unmanned vehicle lane changing decision-making method and system based on adversarial imitation learning. The method comprises the steps that an unmanned vehicle lane changing decision-making task is described as a partially observable Markov decision-making process; training is carried out from examples provided by professional driving teaching by adopting an adversarial imitation learning method to obtain an unmanned vehicle lane changing decision-making model; and in the unmanned driving process of the vehicle, a vehicle lane change decision-making result is obtained through the unmanned vehicle lane change decision-making model by taking currently obtained environmental vehicle information as an input parameter of the unmanned vehicle lane change decision-making model. According to the invention, a lane changing strategy is learned from examples provided by professional driving teaching through adversarial imitation learning; a task reward function does not need to be manually designed, and direct mapping from the vehicle state to the vehicle lane changing decision can be directly established, so that the correctness, robustness and adaptivity of thelane changing decision of the unmanned vehicle under the dynamic traffic flow condition are effectively improved.

Description

technical field [0001] The invention belongs to the technical field of unmanned autonomous vehicles, in particular to a method and system for lane-changing decision-making for unmanned vehicles based on confrontational imitation learning. Background technique [0002] The development of unmanned driving will help improve the level of road traffic intelligence and promote the transformation and upgrading of the transportation industry. Unmanned vehicles are a combination of hardware and software. The hardware includes various types of sensors and controllers, and the software is a comprehensive system that integrates environmental perception, behavior decision-making, motion planning and autonomous control modules. [0003] Lane-changing decision is an important component module of unmanned vehicle decision-making technology, and it is the basis for the execution of subsequent action planning modules. At present, the existing technologies include published patents, and the m...

AI Technical Summary

Problems solved by technology

Both end-to-end supervised learning and deep reinforcement learning can train a neural network model to directly map perception data to lane-changing decision output. However, end-to-end supervised learning usually requires a large amount of training data and the model generalization ability is weak. Reinforcement learning needs to artificially design a reward function that meets the task requirements

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