  Autonomous driving decision-making is a great challenge due to the complexity
and uncertainty of the traffic environment. Combined with the rule-based
constraints, a Deep Q-Network (DQN) based method is applied for autonomous
driving lane change decision-making task in this study. Through the combination
of high-level lateral decision-making and low-level rule-based trajectory
modification, a safe and efficient lane change behavior can be achieved. With
the setting of our state representation and reward function, the trained agent
is able to take appropriate actions in a real-world-like simulator. The
generated policy is evaluated on the simulator for 10 times, and the results
demonstrate that the proposed rule-based DQN method outperforms the rule-based
approach and the DQN method.
