Senthil Yogamani

TL;DR: The proposed framework for autonomous driving using deep reinforcement learning incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios and integrates the recent work on attention models to focus on relevant information, thereby reducing the computational complexity for deployment on embedded hardware.

TL;DR: This review summarises deep reinforcement learning algorithms, provides a taxonomy of automated driving tasks where (D)RL methods have been employed, highlights the key challenges algorithmically as well as in terms of deployment of real world autonomous driving agents, the role of simulators in training agents, and finally methods to evaluate, test and robustifying existing solutions in RL and imitation learning.

TL;DR: In this article, a framework for autonomous driving using deep reinforcement learning is proposed, which incorporates recurrent neural networks for information integration and integrates the recent work on attention models to focus on relevant information, thereby reducing the computational complexity for deployment on embedded hardware.

TL;DR: The first extensive fisheye automotive dataset, WoodScape, named after Robert Wood, which comprises of four surround view cameras and nine tasks including segmentation, depth estimation, 3D bounding box detection and soiling detection is released.

TL;DR: A generic taxonomic survey of semantic segmentation algorithms and then discusses how it fits in the context of automated driving and the particular challenges of deploying it into a safety system which needs high level of accuracy and robustness are listed.