Detrive: Transformer検出器を用いた模倣学習によるエンドツーエンド自動運転
Detrive: Imitation Learning with Transformer Detection for End-to-End Autonomous Driving
Transformerベースの検出モデルを認識部に用いたエンドツーエンド自動運転モデルを提案し、強化学習エージェントを教師とした模倣学習で訓練、動的障害物回避性能の向上を示した。
著者: Daoming Chen, Ning Wang, Feng Chen, Tony Pipe
分類: cs.RO
原文アブストラクト
This Paper proposes a novel Transformer-based end-to-end autonomous driving model named Detrive. This model solves the problem that the past end-to-end models cannot detect the position and size of traffic participants. Detrive uses an end-to-end transformer based detection model as its perception module; a multi-layer perceptron as its feature fusion network; a recurrent neural network with gate recurrent unit for path planning; and two controllers for the vehicle's forward speed and turning angle. The model is trained with an on-line imitation learning method. In order to obtain a better training set, a reinforcement learning agent that can directly obtain a ground truth bird's-eye view map from the Carla simulator as a perceptual output, is used as teacher for the imitation learning. The trained model is tested on the Carla's autonomous driving benchmark. The results show that the Transformer detector based end-to-end model has obvious advantages in dynamic obstacle avoidance compared with the traditional classifier based end-to-end model.
