Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Coupling Intent and Action for Pedestrian Crossing Behavior Prediction
Authors: Yu Yao, Ella Atkins, Matthew Johnson-Roberson, Ram Vasudevan, Xiaoxiao Du
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated our approach on two naturalistic driving datasets, PIE and JAAD, and extensive experiments show significantly improved and more explainable results for both intent detection and action prediction over state-of-the-art approaches. |
| Researcher Affiliation | Academia | Yu Yao , Ella Atkins , Matthew Johnson-Roberson , Ram Vasudevan and Xiaoxiao Du University of Michigan EMAIL |
| Pseudocode | No | The proposed architecture is illustrated in Figure 2. |
| Open Source Code | Yes | Our code is available at: https://github.com/umautobots/ pedestrian intent action detection |
| Open Datasets | Yes | We evaluate the effectiveness of our method for intent detection and action prediction on two publicly available naturalistic traffic video benchmarks, Pedestrian Intent Estimation (PIE) [Rasouli et al., 2019] and Joint Attention in Autonomous Driving (JAAD) [Kotseruba et al., 2016]. |
| Dataset Splits | Yes | The PIE dataset was collected with an on-board camera covering six hours of driving footage. There are 1,842 pedestrians (880/243/719 for training/validation/test) with 2-D bounding boxes annotated at 30Hz with behavioral tags. Ego-vehicle velocity readings were provided using gyroscope readings from the camera. The JAAD dataset contains 346 videos with 686 pedestrians (188/32/126) captured from dashboard cameras, annotated at 30Hz with crossing intent annotations. |
| Hardware Specification | Yes | All models were trained with sample length T = 30, prediction horizon δ = 5, learning rate 1e 5, and batch size 128 on a NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The proposed neural network model was implemented using Py Torch. |
| Experiment Setup | Yes | All models were trained with sample length T = 30, prediction horizon δ = 5, learning rate 1e 5, and batch size 128 on a NVIDIA Tesla V100 GPU. |