FEN-DQN: An End-to-End Autonomous Driving Framework Based on Reinforcement Learning with Explicit Affordance

The slow convergence rate is a thorny problem of current end-to-end autonomous driving paradigm with various traffic elements and tasks. In this paper, we propose an end-to-end autonomous driving framework FEN-DQN to simplify problems involving feature extraction network (FEN) with explicit affordance, along with some associated driving measurements such as vehicle speed and position. The FEN-DQN can be divided into two part. First, FEN is applied to map the forward-looking camera images into explicit affordances, which represent traffic information in low-dimension. Secondly, a deep Q-network (DQN) is used to map explicit affordances to vehicle actions. Based on the CARLA simulator, we use the OpenAI-Gym to construct a simulation scenario at traffic intersections to evaluate our proposed framework. In addition, we also conduct comparative experiments on different inputs to show the excellent effect of our framework. The results showcase that FEN-DQN could converge faster and perform better compared with the other inputs at traffic intersections in the simulation scenario with the assistance of FEN.