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arXiv · 2310.14224

Detrive: Imitation Learning with Transformer Detection for End-to-End Autonomous Driving

Daoming Chen·Ning Wang·Feng Chen·Tony Pipe
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Abstract

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.

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Daoming Chen, Ning Wang, Feng Chen, Tony Pipe. 2023-10-22. Detrive: Imitation Learning with Transformer Detection for End-to-End Autonomous Driving. https://arxiv.org/abs/2310.14224

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