Papers › LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous...

LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learning

19 Oct 2023arXiv:2310.13135archive 2025-07-28

Pedram Agand, Mohammad Mahdavian, Manolis Savva, Mo Chen

In end-to-end autonomous driving, the utilization of existing sensor fusion techniques and navigational control methods for imitation learning proves inadequate in challenging situations that involve numerous dynamic agents. To address this issue, we introduce LeTFuser, a lightweight transformer-based algorithm for fusing multiple RGB-D camera representations. To perform perception and control tasks simultaneously, we utilize multi-task learning. Our model comprises of two modules, the first being the perception module that is responsible for encoding the observation data obtained from the RGB-D cameras. Our approach employs the Convolutional vision Transformer (CvT) \cite{wu2021cvt} to better extract and fuse features from multiple RGB cameras due to local and global feature extraction capability of convolution and transformer modules, respectively. Encoded features combined with static and dynamic environments are later employed by our control module to predict waypoints and vehicular controls (e.g. steering, throttle, and brake). We use two methods to generate the vehicular controls levels. The first method uses a PID algorithm to follow the waypoints on the fly, whereas the second one directly predicts the control policy using the measurement features and environmental state. We evaluate the model and conduct a comparative analysis with recent models on the CARLA simulator using various scenarios, ranging from normal to adversarial conditions, to simulate real-world scenarios. Our method demonstrated better or comparable results with respect to our baselines in term of driving abilities. The code is available at \url{https://github.com/pagand/e2etransfuser/tree/cvpr-w} to facilitate future studies.

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Tasks

Autonomous Driving Imitation Learning Multi-Task Learning Semantic Segmentation Sensor Fusion

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Absolute Position Encodings Adam Attention BPE CARLA Convolution Dense Connections Dropout Entropy Regularization Label Smoothing Layer Normalization Linear Layer Multi-Head Attention PPO Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer Vision Transformer

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