Driver and Pedestrian Mutual Awareness for Path Prediction and Collision Risk Estimation

We present a novel method for vehicle-pedestrian path prediction that takes into account the awareness of the driver and the pedestrian towards each other. The method jointly models the paths of vehicle and pedestrian within a single Dynamic Bayesian Network (DBN). In this DBN, sub-graphs model the...

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Bibliographic Details
Published in:IEEE transactions on intelligent vehicles 2022-12, Vol.7 (4), p.896-907
Main Authors: Roth, Markus, Stapel, Jork, Happee, Riender, Gavrila, Dariu M.
Format: Article
Language:English
Subjects:
Ablation
Bayesian analysis
Collision detection
Collision dynamics
Collision risk estimation
Context
Context modeling
driver awareness
Dynamic characteristics
Dynamics
Graphs
Horizon
Intelligent vehicles
path prediction
Path predictors
pedestrian awareness
Predictive models
Risk
Road traffic
Vehicle dynamics
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Summary:We present a novel method for vehicle-pedestrian path prediction that takes into account the awareness of the driver and the pedestrian towards each other. The method jointly models the paths of vehicle and pedestrian within a single Dynamic Bayesian Network (DBN). In this DBN, sub-graphs model the environment and entity-specific context cues of the vehicle and pedestrian (incl. awareness), which affect their future motion and allow to increase the prediction horizon. These sub-graphs share a latent state which models whether vehicle and pedestrian are on collision course; this accounts for a certain degree of motion coupling. The method was validated with real-world data obtained by on-board vehicle sensing (stereo vision, GNSS and proprioceptive). Data consist of 93 vehicle and pedestrian encounters, spanning various awareness conditions and dynamic characteristics of the participants. In ablation studies, we quantify the benefits of various components of our proposed DBN model for path prediction and collision risk estimation. Results show that at a prediction horizon of 1.5 s, context-aware models outperform context-agnostic models in path prediction for scenarios with a dynamics change, while performing similarly otherwise. Results further indicate that driver attention-aware models improve collision risk estimation compared to driver-agnostic models.
ISSN:2379-8858
2379-8904
DOI:10.1109/TIV.2021.3138944