  Autonomous driving has received a lot of attention in the automotive industry
and is often seen as the future of transportation. Passenger vehicles equipped
with a wide array of sensors (e.g., cameras, front-facing radars, LiDARs, and
IMUs) capable of continuous perception of the environment are becoming
increasingly prevalent. These sensors provide a stream of high-dimensional,
temporally correlated data that is essential for reliable autonomous driving.
An autonomous driving system should effectively use the information collected
from the various sensors in order to form an abstract description of the world
and maintain situational awareness. Deep learning models, such as autoencoders,
can be used for that purpose, as they can learn compact latent representations
from a stream of incoming data. However, most autoencoder models process the
data independently, without assuming any temporal interdependencies. Thus,
there is a need for deep learning models that explicitly consider the temporal
dependence of the data in their architecture. This work proposes CARNet, a
Combined dynAmic autoencodeR NETwork architecture that utilizes an autoencoder
combined with a recurrent neural network to learn the current latent
representation and, in addition, also predict future latent representations in
the context of autonomous driving. We demonstrate the efficacy of the proposed
model in both imitation and reinforcement learning settings using both
simulated and real datasets. Our results show that the proposed model
outperforms the baseline state-of-the-art model, while having significantly
fewer trainable parameters.
