mlsanityData to 7 Oct 2026

Joint Attention in Autonomous Driving (JAAD)

arXiv 1609.04741 ↗First posted 15 Sep 2016cs.ROcited by 35

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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.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 2; 2023: 10; 2024: 4; 2025: 5; 2026: 7.

35 papers.

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  1. 2021Stepwise Goal-Driven Networks for Trajectory Predictioncited by 62
  2. 2021Is attention to bounding boxes all you need for pedestrian action prediction?cited by 21
  3. 2025Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Drivingcited by 19
  4. 2021PSI: A Benchmark for Human Interpretation and Response in Traffic Interactionscited by 17
  5. 2021Coupling Intent and Action for Pedestrian Crossing Behavior Predictioncited by 14
  6. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  7. 2023Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?cited by 13
  8. 2023Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypointscited by 12
  9. 2021Multi-Agent Variational Occlusion Inference Using People as Sensorscited by 10
  10. 2024Human Action Anticipation: A Surveycited by 8
  11. 2021Review of Video Predictive Understanding: Early Action Recognition and Future Action Predictioncited by 5
  12. 2022Pedestrian Stop and Go Forecasting with Hybrid Feature Fusioncited by 5
23 more
  1. 2023Context-aware Pedestrian Trajectory Prediction with Multimodal Transformercited by 4
  2. 2023The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Datasetcited by 3
  3. 2024Context-aware Multi-task Learning for Pedestrian Intent and Trajectory Predictioncited by 3
  4. 2025Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Surveycited by 3
  5. 2025DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Riskscited by 3
  6. 2023A Survey on Datasets for Decision-making of Autonomous Vehiclecited by 2
  7. 2023Pedestrian and Passenger Interaction with Autonomous Vehicles: Field Study in a Crosswalk Scenariocited by 2
  8. 2025MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understandingcited by 2
  9. 2026VIT-Ped: Visionary Intention Transformer for Pedestrian Behavior Analysiscited by 2
  10. 2022"If you could see me through my eyes": Predicting Pedestrian Perceptioncited by 1
  11. 2023SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Drivingcited by 1
  12. 2024ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Drivingcited by 1
  13. 2026A global dataset of continuous urban dashcam drivingcited by 1
  14. 2021GPRAR: Graph Convolutional Network based Pose Reconstruction and Action Recognition for Human Trajectory Predictioncited by 0
  15. 2023Learning Trajectory-Conditioned Relations to Predict Pedestrian Crossing Behaviorcited by 0
  16. 2023Spatiotemporal-Linear: Towards Universal Multivariate Time Series Forecastingcited by 0
  17. 2024A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Predictioncited by 0
  18. 2025EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Drivingcited by 0
  19. 2026Single-Eye View: Monocular Real-time Perception Package for Autonomous Drivingcited by 0
  20. 2026No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Controlcited by 0
  21. 2026Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videoscited by 0
  22. 2026LLM-Driven Autonomous Vehicles Inherit Human Driver Biases in Pedestrian Yielding: Results and Implications From A New Benchmarkcited by 0
  23. 2026Wave Function Backpropagation with Explicit Temporal-Interval Dynamicscited by 0