In this paper, we propose a survey of the Simultaneous Localization And Mapping (SLAM)
field when considering the recent evolution of autonomous driving. The growing interest …

Numerical experiments for motion planning of road vehicles require numerous components:
vehicle dynamics, a road network, static obstacles, dynamic obstacles and their movement …

We present a reinforcement learning approach using Deep Q-Networks to steer a vehicle in
a 3D physics simulation. Relying solely on camera image input the approach directly learns …

Recent accidents involving autonomous vehicles prompt us to consider how we can
engineer an autonomous vehicle which always obeys traffic rules. This is particularly …

Predicting the movement of other traffic participants is an integral part in the motion planning
of most automated road vehicles. While simple predictions, eg based on assuming constant …

Localization within high definition maps is a key problem for autonomous navigation as
vehicles need to extract information from them. In addition, many navigation tasks are …

Navigation maps provide important information for Advanced Driving Assistance Systems
(ADAS) and Autonomous Vehicles. This paper presents a method estimating a set of likely …

In order to determine a cooperative driving strategy, it is beneficial for an autonomous
vehicle to incorporate the intended motion of surrounding vehicles within its own motion …

A crucial capability of autonomous road vehicles is the ability to cope with the unknown
future behavior of surrounding traffic participants. This requires using non-deterministic …

Safe and human-like path planning in urban environments remain one of the most
challenging problems for autonomous driving. This paper proposes a solution to plan traffic …