|Place of Conferral||中国科学院自动化研究所|
|Keyword||无人车控制 自主学习 规则提取 规则迭代|
In recent years, autonomous driving has attracted widespread attention in the world. Unmanned driving technology is of great significance to the improvement of urban traffic and the sustainable development of environmental friendliness. At the same time, it can also enhance driving safety and effectively reduce the accident rate. Unmanned vehicle motion control is an indispensable technology in the field of autonomous driving. Different from the traditional solutions of autonomous driving technology, which are divided into perception, cognition, decision, and control, the emergence of AlphaGo Zero set up a new paradigm for the research of machine self-learning ability. In order to enable the autonomous vehicle controller to have safe and smooth driving ability through self-learning, the paper references to the process of human learning driving and carries out research on the rule-based iterative self-learning control method of unmanned vehicle.
In this paper, based on deep reinforcement learning method, and through rule extraction and reward rule iteration, We imitated the gradual process of human learning to drive a vehicle, and realized the safe and stable running of the unmanned vehicle in the simulation environment. Its main works are as follows:
(1) Building Carla simulation platform and carrying out simulation experiments for the motion control task of unmanned vehicles in urban road environment to verify the performance of two typical deep reinforcement learning algorithms, DQN and DDPG.
(2) Analysing of vehicle driving tasks under typical urban road environment, according to traffic rules and road boundary conditions, a set of hierarchical driving task rules is given, which is transformed into the corresponding reward and punishment rules in the learning algorithm.
(3) The reward function of the DDPG algorithm is improved based on the obtained reward and punishment rules, and the state space of DDPG algorithm is optimized. Experimental result shows that the proposed method effectively improves the stability of the unmanned vehicle controller and the training efficiency of the algorithm. The average completion degree of the vehicle's driving task is close to 90%, and the training time is shortened. Compared with the training results of DQN and DDPG, the results were significantly improved.
|张力夫. 一种基于规则迭代的无人车自学习控制方法[D]. 中国科学院自动化研究所. 中国科学院大学,2021.|
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