Interacting with other roads users is a challenge for an autonomous vehicle, particularly in
urban areas. Existing vehicle systems behave in a reactive manner, warning the driver or …

Predicting Vulnerable Road User (VRU) crossing intention is one of the major challenges in
automated driving. Crossing intention prediction systems trained only on pedestrian features …

In autonomous driving systems, one of the most crucial aspects is to anticipate the
movements of other traffic participants. In this paper, several machine learning methods are …

With the rapid development of the techniques for autonomous driving and ADAS in the last
decade, more advanced methods to understand pedestrian behavior are required …

Predicting Vulnerable Road User (VRU) crossing intention is one of the major challenges in
automated driving. Crossing intention prediction systems trained only on pedestrian features …

The pedestrian crossing intention prediction problem is to estimate whether or not the target
pedestrian will cross the street. State-of-the-art techniques heavily depend on visual data …

In inner-city, most vehicle-pedestrian collisions occur when a pedestrian is crossing the road
and the driver does not see or pay attention to him. Current ADAS (advanced driver …

Predicting pedestrian crossing trajectories has become a primary task in aiding autonomous
vehicles to assess risks in pedestrian–vehicle interactions. As agile participants with …

Future automated driving systems will require a comprehensive scene understanding.
Considering these systems in an urban environment it becomes immediately clear that …

The ability to anticipate pedestrian actions on streets is a safety issue for intelligent cars and
has increasingly drawn the attention of the automotive industry. Estimating when …