Coupling Intent and Action for Pedestrian Crossing Behavior Prediction
Authors: Yu Yao, Ella Atkins, Matthew Johnson-Roberson, Ram Vasudevan, Xiaoxiao Du
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 {brianyao, ematkins, mattjr, ramv, xiaodu}@umich.edu |
| 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. |