Scaling Vision-based End-to-End Driving with Multi-View Attention Learning

• URL: http://arxiv.org/abs/2302.03198v3
• Date: Sat, 22 Jul 2023 14:01:00 GMT
• Title: Scaling Vision-based End-to-End Driving with Multi-View Attention Learning
• Authors: Yi Xiao, Felipe Codevilla, Diego Porres, Antonio M. Lopez
• Abstract summary: We present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning.
• Score: 7.14967754486195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning.

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