Joint Attention in Autonomous Driving (JAAD)
This addresses the need for better data on human interactions in traffic for autonomous driving systems, but it is incremental as it focuses on dataset creation without new methods.
The authors introduced a novel dataset for joint attention in autonomous driving, capturing behavioral variability of traffic participants and analyzing how visual complexity and scene understanding are affected by factors like weather and demographics.
In this paper we present a novel dataset for a critical aspect of autonomous driving, the joint attention that must occur between drivers and of pedestrians, cyclists or other drivers. This dataset is produced with the intention of demonstrating the behavioral variability of traffic participants. We also show how visual complexity of the behaviors and scene understanding is affected by various factors such as different weather conditions, geographical locations, traffic and demographics of the people involved. The ground truth data conveys information regarding the location of participants (bounding boxes), the physical conditions (e.g. lighting and speed) and the behavior of the parties involved.