# Joint Attention in Autonomous Driving (JAAD)

arXiv: https://arxiv.org/abs/1609.04741 · HTML: https://mlsanity.com/paper/1609.04741/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2016.

## Built on by (most cited first, 30 of 36)

- [Stepwise Goal-Driven Networks for Trajectory Prediction](https://mlsanity.com/paper/2103.14107/) (2021)
- [Is attention to bounding boxes all you need for pedestrian action prediction?](https://mlsanity.com/paper/2107.08031/) (2021)
- [Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving](https://mlsanity.com/paper/2503.21505/) (2025)
- [PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions](https://mlsanity.com/paper/2112.02604/) (2021)
- [Coupling Intent and Action for Pedestrian Crossing Behavior Prediction](https://mlsanity.com/paper/2105.04133/) (2021)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?](https://mlsanity.com/paper/2306.01220/) (2023)
- [Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints](https://mlsanity.com/paper/2306.01075/) (2023)
- [Multi-Agent Variational Occlusion Inference Using People as Sensors](https://mlsanity.com/paper/2109.02173/) (2021)
- [Human Action Anticipation: A Survey](https://mlsanity.com/paper/2410.14045/) (2024)
- [Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction](https://mlsanity.com/paper/2107.05140/) (2021)
- [Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion](https://mlsanity.com/paper/2203.02489/) (2022)
- [Context-aware Pedestrian Trajectory Prediction with Multimodal Transformer](https://mlsanity.com/paper/2307.03786/) (2023)
- [The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset](https://mlsanity.com/paper/2307.06165/) (2023)
- [Context-aware Multi-task Learning for Pedestrian Intent and Trajectory Prediction](https://mlsanity.com/paper/2407.17162/) (2024)
- [Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2509.10570/) (2025)
- [DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks](https://mlsanity.com/paper/2511.14592/) (2025)
- [A Survey on Datasets for Decision-making of Autonomous Vehicle](https://mlsanity.com/paper/2306.16784/) (2023)
- [Pedestrian and Passenger Interaction with Autonomous Vehicles: Field Study in a Crosswalk Scenario](https://mlsanity.com/paper/2312.07606/) (2023)
- [MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding](https://mlsanity.com/paper/2507.12463/) (2025)
- [VIT-Ped: Visionary Intention Transformer for Pedestrian Behavior Analysis](https://mlsanity.com/paper/2601.01989/) (2026)
- ["If you could see me through my eyes": Predicting Pedestrian Perception](https://mlsanity.com/paper/2202.13981/) (2022)
- [SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving](https://mlsanity.com/paper/2306.03538/) (2023)
- [ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving](https://mlsanity.com/paper/2411.01683/) (2024)
- [A global dataset of continuous urban dashcam driving](https://mlsanity.com/paper/2604.01044/) (2026)
- [GPRAR: Graph Convolutional Network based Pose Reconstruction and Action Recognition for Human Trajectory Prediction](https://mlsanity.com/paper/2103.14113/) (2021)
- [Learning Trajectory-Conditioned Relations to Predict Pedestrian Crossing Behavior](https://mlsanity.com/paper/2301.05796/) (2023)
- [Spatiotemporal-Linear: Towards Universal Multivariate Time Series Forecasting](https://mlsanity.com/paper/2312.14869/) (2023)
- [A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2406.18050/) (2024)
- [EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving](https://mlsanity.com/paper/2502.19260/) (2025)

## Abstract (arXiv)

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.