Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories
Abstract
:1. Introduction
- Utilization of random forest (RF) for detailed pedestrian intention forecasting using naturalistic trajectories that were captured using a drone;
- An algorithm to automate the addition of pedestrian crossing intent labels to the existing dataset (see Section 4.1);
- Extensive and detailed validation and evaluation of pedestrian trajectories extracted from real data to show that the proposed model is applicable at different crossing locations, not just a predefined ROI (e.g., crosswalk). Scenarios where the crossing intention is not clear are also investigated (e.g., pedestrian slowing down before crossing);
- Comparison analysis between an RF and a feed-forward neural network (NN) in the context of pedestrian intention forecasting, verifying that the proposed approach outperforms the NN.
2. Related Work
2.1. On-Board Sensors
2.2. Infrastructure Sensors
3. Methodology
3.1. Random Forest
3.2. Dataset
4. Experiments and Results
4.1. Data Preprocessing
4.2. Training
4.3. Evaluation
- C Pedestrian at the zebra crossing, ID: 42, 121, 162, 273;
- C Pedestrian at the bottom arm of the intersection, ID: 4, 100, 242;
- C Pedestrians on less utilized (i.e., walked) areas (left and top arms of the intersection), ID: 1, 171, 299.
- Pedestrians walking on the sidewalk with NC intention, ID: 193, 369;
- NC while walking in front of the zebra-crossing, ID: 63, 77, 148, 183, 203_1, 242;
- Pedestrian walking away from the crosswalk, ID: 248;
- Pedestrian turning and NC in front of the road, ID: 203_2.
4.4. When Pedestrian Intention Is Not Clear
5. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Hyperparameter | Value |
---|---|
Number of hidden layers | 3 |
Number of units per layer | 1000 |
Activation function | ReLU |
Solver | Adam |
Alpha | 0.05 |
Learning rate | Constant |
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Moreno, E.; Denny, P.; Ward, E.; Horgan, J.; Eising, C.; Jones, E.; Glavin, M.; Parsi, A.; Mullins, D.; Deegan, B. Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories. Sensors 2023, 23, 2773. https://doi.org/10.3390/s23052773
Moreno E, Denny P, Ward E, Horgan J, Eising C, Jones E, Glavin M, Parsi A, Mullins D, Deegan B. Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories. Sensors. 2023; 23(5):2773. https://doi.org/10.3390/s23052773
Chicago/Turabian StyleMoreno, Esteban, Patrick Denny, Enda Ward, Jonathan Horgan, Ciaran Eising, Edward Jones, Martin Glavin, Ashkan Parsi, Darragh Mullins, and Brian Deegan. 2023. "Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories" Sensors 23, no. 5: 2773. https://doi.org/10.3390/s23052773