In order to ensure reliable autonomous driving, the system must be able to detect future
dangers in sufficient time to avoid or mitigate collisions. In this paper, we propose a collision …

Predicting future motions of surrounding vehicles and driver's intentions are essential to
avoid future potential risks. The predicting future motions, however, is very challenging …

Situational assessment (SA) is one of the key parts for the application of intelligent
alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand …

Four major approaches to robot motion planning in dynamic environments are discussed:
probabilistic robot, probabilistic collision state (PCS), partially closed-loop receding horizon …

This paper addresses the proposal of a new multidimensional risk estimator using spatial
uncertainty modeling for automated vehicles risk assessment. This risk estimator, on which …

This thesis introduces fail-safe motion planning as the first approach to guarantee legal
safety of autonomous vehicles in arbitrary traffic situations. The proposed safety layer …

The goal of autonomous driving is to increase safety, benefit and comfort for all road users.
Above all, the area of risk perception is of central importance in preventing critical situations …

The prevention of risky situations is one of the main tasks in autonomous driving (AD) and
intelligent driving assistant systems (ADAS). Uncertainty in the traffic participants' behavior …

Avoiding critical situations is a prerequisite for Advanced Driver Assistant Systems and
Autonomous Driving to decrease the number of total hazards and fatal collisions. As a guide …

Q-learning (QL) approach is constantly used for mobile robot (MR) navigation in unknown
dynamic environment because of its simplicity and well-developed theory. However, its …