Predicting pedestrian path using optical flow as context cue

A human driver can gauge the intention and signals given by other road users indicative of their future behaviour. The intentions and signals are identified by looking at the cues originating from vulnerable road users or their surroundings (hand signals, head orientation, posture, traffic signals, distance to curb, etc.). Taking all these cues...

3D Object Detection For Intelligent Vehicles

This master thesis presents an experimental study on 3D person localization (i.e., pedestrians, cyclists)in traffic scenes, using monocular vision and Light Detection And Ranging (LiDAR) data. The performance of two top-ranking methods is analyzed on the 3D object detection KITTI dataset. In this evaluation, the effect of the Intersection over...

Safety assessment of the interaction between an automated vehicle and a cyclist

The operation of automated vehicles in shared areas requires attention with respect to the interaction between AVs and vulnerable road users, including cyclists. Currently, the programmed interaction behavior of AVs is based on the knowledge of the interaction between conventional vehicles and cyclists. However, cyclists may react differently to...

Determinants of a safe interaction between Vulnerable Road Users and Automated Vehicles using Fuzzy Cognitive Mapping

The technology of automated driving systems that assist the human driver are in ongoing development and could potentially improve traffic safety and efficiency. At this moment, a lot of research into automated vehicles is carried out. The City of Amsterdam wants to know what impact AVs can have on traffic safety in their city. Most studies focus...