Real-Time Age Detection Using a Convolutional Neural Network

Sithungu, Siphesihle; Haar, Dustin van der

doi:10.1007/978-3-030-20482-2_20

Cited by 4 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is possible that when trained on larger data sets, the model might show different preferences for different types of pedestrians. For example, real-time detectable features such as age [36], gender [48], body pose [12], activity recognition [14], gait [44], and style of dress might give information about pedestrian intention and behavioural preferences, which if found from training data could then be used to refine real-time AVs pedestrian predictions and active speed controls. This method could then possibly enable new AV online-learning algorithms that adapt to the environment or passenger's preferences.…”

Section: Discussionmentioning

confidence: 99%

Continuous Game Theory Pedestrian Modelling Method for Autonomous Vehicles

Camara¹,

Coşar²,

Bellotto³

et al. 2022

Human Factors in Intelligent Vehicles

View full text Add to dashboard Cite

Autonomous Vehicles (AVs) must interact with other road users. They must understand and adapt to complex pedestrian behaviour, especially during crossings where priority is not clearly defined. This includes feedback effects such as modelling a pedestrian's likely behaviours resulting from changes in the AVs behaviour. For example, whether a pedestrian will yield if the AV accelerates, and vice versa. To enable such automated interactions, it is necessary for the AV to possess a statistical model of the pedestrian's responses to its own actions. A previous work demonstrated a proof-of-concept method to fit parameters to a simplified model based on data from a highly artificial discrete laboratory task with human subjects. The method was based on lidar-based person tracking, game theory, and Gaussian process analysis. The present study extends this method to enable analysis of more realistic continuous human experimental data. It shows for the first time how game-theoretic predictive parameters can be fit into pedestrians natural and continuous motion during road-crossings, and how predictions can be made about their interactions with AV controllers in similar real-world settings.

show abstract