… taxonomy of automated driving tasks where … autonomous driving agents. It also delineates
adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning …

… learning to the problem of forming long term driving strategies. We note that there are two
major challenges that make autonomous driving … something that machine learning has difficulty …

Advances in information and signal processing technologies have a significant impact on
autonomous driving (AD), improving driving safety while minimizing the efforts of human …

… In order to bridge the gap between autonomous driving and reinforcement learning, we
adopt the deep deterministic policy gradient (DDPG) algorithm to train our agent in The Open …

… components of autonomous driving. … learning technologies used in autonomous driving,
and provide a survey on state-of-the-art deep learning and AI methods applied to self-driving …

… policy learning. However, training autonomous driving vehicle with reinforcement learning
in … Given realistic frames as input, driving policy trained by reinforcement learning can nicely …

… In this paper we present the Audi Autonomous Driving … tant for further advances in autonomous
driving, and therefore have … research in autonomous driving, eg end-to-end learning as …

… vision- based autonomous driving with deep learning and reinforcement learning methods.
… As one of the most popular topics, autonomous driving has drawn great attention both from …

… In this paper, we proposed a simulation study of an autonomous agent learning to drive
in a simplified environment consisting of only lane markings and static obstacles. We used a …

… and limitation in the deep learningbased ADSs. This survey discusses various adversarial
attacks that could be detrimental to deep learning autonomous driving models and identify …