Joint Attention in Autonomous Driving (JAAD)

TL;DR

This paper introduces a new dataset for joint attention in autonomous driving, capturing behavioral variability and environmental factors affecting traffic participants to improve scene understanding.

Contribution

The paper presents a novel dataset focusing on joint attention behaviors in autonomous driving, including diverse conditions and detailed annotations for scene understanding.

Findings

Behavioral variability of traffic participants is significant.

Environmental factors influence scene complexity.

Dataset enables better modeling of driver and pedestrian interactions.

Abstract

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.