Offline tracking with object permanence

Learning-based approaches are widely applied in the perception system of autonomous vehicles. Thus, a large amount of labeled data are needed to train these data-hungry models. To reduce the expensive labor cost for manual labeling autonomous driving datasets, an alternative is to automatically annotate the datasets using a trained offline...

Gaze-contingent interfaces: The effect on noticing times of time critical warning messages under Level 2 driving conditions while interacting with in-vehicle technology

With the introduction of automated driving systems come benefits such as the improvement of traffic safety. However, with an increasing level of automation in vehicles also comes an increase in interaction with in-vehicle technology by drivers while they are meant to supervise the automated driving systems. Due to more interaction with in...

Measuring driver perception during on-road eye-tracking: Combining gaze behaviour and vehicle’s road scene perception

Long before humans will completely trust fully automated vehicles, partial and conditional automation where the human driver is still in the loop will dominate the era of autonomous vehicles. However, more than 90% of traffic accidents are due to human errors, of which approximately half appear to be due to perceptual errors. Especially at busy...

3D-LIDAR Multi Object Tracking for Autonomous Driving: Multi-target Detection and Tracking under Urban Road Uncertainties

The recent advancement of the autonomous vehicle has raised the need for reliable environmental perception. This is evident, as an autonomous vehicle has to perceive and interpret its local environment in order to execute reactive and predictive control action. Object Tracking is an integral part of vehicle perception, as it enables the vehicle...