There is an increasing level of interest in self-driving cars. In fact, it is predicted that fully
autonomous cars will roam the streets by 2020. For an autonomous car to drive by itself, it …

Self-driving cars rely on a plethora of algorithms in order to perform safe driving
manoeuvres. Training those models is expensive (eg hardware cost, storage, energy) and …

In this work we present a simple end-to-end trainable machine learning system capable of
realistically simulating driving experiences. This can be used for verification of self-driving …

As driving task has been and still is the domain of humans, they are a relatively good model
of how to behave on road. However, as we would like to constantly raise standards of safety …

End-to-end approaches to autonomous driving have high sample complexity and are difficult
to scale to realistic urban driving. Simulation can help end-to-end driving systems by …

Recently, deep neural networks trained with Imitation-Learning techniques have managed
to successfully control autonomous cars in a variety of urban and highway environments …

Training autonomous vehicles require rigorous and comprehensive testing to deal with a
variety of situations that they expect to undergo on roads in real-time. The physical testing of …

Self Driving Vehciles (SDV) experienced fast development during the last decades with the
joint rise of deep learning and high speed computation technologies. Currently, SDV are …

Over the past few years there is a growing interest in the learning-based self driving system.
To ensure safety, such systems are first developed and validated in simulators before being …

Autonomous vehicles which are capable of operating independently will be commercially
available in the near future. Autonomous driving systems are becoming more complicated …