Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice

Abstract

Today, one of the major challenges that autonomous vehicles are facing is the ability to drive in urban environments. Such a task requires communication between autonomous vehicles and other road users in order to resolve various traffic ambiguities. The interaction between road users is a form of negotiation in which the parties involved have to share their attention regarding a common objective or a goal (e.g. crossing an intersection), and coordinate their actions in order to accomplish it. In this literature review we aim to address the interaction problem between pedestrians and drivers (or vehicles) from joint attention point of view. More specifically, we will discuss the theoretical background behind joint attention, its application to traffic interaction and practical approaches to implementing joint attention for autonomous vehicles.

References in corpus (12)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
• Multiple Object Recognition with Visual Attention
• Video Salient Object Detection via Fully Convolutional Networks
• Ten Years of Pedestrian Detection, What Have We Learned?
• Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
• CityPersons: A Diverse Dataset for Pedestrian Detection
• Deep Deconvolutional Networks for Scene Parsing
• Agreeing to Cross: How Drivers and Pedestrians Communicate
• Integrating Three Mechanisms of Visual Attention for Active Visual Search
• When Vehicles See Pedestrians with Phones:A Multi-Cue Framework for Recognizing Phone-based Activities of Pedestrians
• Attentional Push: Augmenting Salience with Shared Attention Modeling

Cited by in corpus (4)

• Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
• IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture
• Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving
• DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark