  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.
