Automatic driving system backdoor attack method based on deep reinforcement learning and related device

The invention discloses an automatic driving system backdoor attack method based on deep reinforcement learning and a related device. The method comprises the following steps: determining a threat model according to attacker ability and a target; under the threat model, determining a state space, an action space and a reward function of the deep reinforcement learning model, and designing a malicious reward function of backdoor attack; designing a backdoor trigger on the basis of the malicious reward function, and hiding the backdoor trigger in a series of continuous space-time states; and fusing the malicious reward function and the backdoor trigger into the training process of the deep reinforcement learning model, configuring training parameters, and training and deploying the deep reinforcement learning model with the backdoor. According to the method, the backdoor attack is performed on the deep reinforcement learning by using the time and space characteristics of vehicle driving, the attack success rate