  The problem of action-conditioned image prediction is to predict the expected
next frame given the current camera frame the robot observes and an action
selected by the robot. We provide the first comparison of two recent popular
models, especially for image prediction on cars. Our major finding is that
action tiling encoding is the most important factor leading to the remarkable
performance of the CDNA model. We present a light-weight model by action tiling
encoding which has a single-decoder feedforward architecture same as
[action_video_prediction_honglak]. On a real driving dataset, the CDNA model
achieves ${0.3986} \times 10^{-3}$ MSE and ${0.9846}$ Structure SIMilarity
(SSIM) with a network size of about {\bfseries ${12.6}$ million} parameters.
With a small network of fewer than {\bfseries ${1}$ million} parameters, our
new model achieves a comparable performance to CDNA at ${0.3613} \times
10^{-3}$ MSE and ${0.9633}$ SSIM. Our model requires less memory, is more
computationally efficient and is advantageous to be used inside self-driving
vehicles.
