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Joint Attention in Autonomous Driving (JAAD)

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arxiv 1609.04741 v6 pith:NQW4RXJY submitted 2016-09-15 cs.RO

Iuliia Kotseruba , Amir Rasouli , John K. Tsotsos This is my paper · ORCID
classification cs.RO
keywords attentionautonomousconditionsdatasetdriversdrivinginvolvedjoint
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 83 citations worldwide. Full citation record

  1. Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    MMPM uses PIM for gaze/head/hand interactions and MTP (CVAE with query decoder) to model separate crossing/non-crossing trajectory distributions, outperforming baselines on PIE and JAAD with a new validation protocol.

  2. MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MMHU introduces a large-scale multimodal benchmark with 57k human instances and rich annotations for motion, trajectory, text, behavior labels, and VQA in driving scenes.

  3. DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks

    cs.RO 2025-11 reject novelty 5.0 of 10

    A new benchmark claims to be the first to test VLMs on both external and in-cabin driving risks, and reports a fine-tuned model far outperforming all baselines.

  4. Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey

    cs.RO 2025-09 conditional novelty 4.0 of 10

    A structured survey of LLM-based trajectory prediction methods, organized into trajectory-language mapping, multimodal fusion, and constraint-based reasoning, with benchmarks, metrics, and future directions.

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