<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Automated cargo bikes are intended to complement public transportation in a sharing concept and provide an alternative transportation option for people and goods. In highly automated driving without a seated user, real-time trajectory prediction of other road users is crucial for collision avoidance with other motor vehicles or vulnerable road users (VRU). For this purpose, moving obstacles are detected by environmental sensors and classified and tracked using object detection and tracking algorithms. The current and past position data as well as environmental information are used to predict future positions. In this paper, we present several AI-based trajectory prediction models that are specifically suited for this use case. Our focus is not only on the accuracy of trajectory prediction, but additionally on a robust, real-time and practical application. We consider models that can predict the trajectories with position estimation or distributions for position estimation for each time step in the future. For this aim, we present generative network structures based on Conditional Variational Autoencoder (CVAE) in different variants. After training, the models are integrated into our production system and their computation time is determined on the hardware we use.</p>
</abstract>

Automated cargo bikes are intended to complement public transportation in a sharing concept and provide an alternative transportation option for people and goods. In highly automated driving without a seated user, real-time trajectory prediction of other road users is crucial for collision avoidance with other motor vehicles or vulnerable road users (VRU). For this purpose, moving obstacles are detected by environmental sensors and classified and tracked using object detection and tracking algorithms. The current and past position data as well as environmental information are used to predict future positions. In this paper, we present several AI-based trajectory prediction models that are specifically suited for this use case. Our focus is not only on the accuracy of trajectory prediction, but additionally on a robust, real-time and practical application. We consider models that can predict the trajectories with position estimation or distributions for position estimation for each time step in the future. For this aim, we present generative network structures based on Conditional Variational Autoencoder (CVAE) in different variants. After training, the models are integrated into our production system and their computation time is determined on the hardware we use.

Autonomous Cargo Bike Fleets – Approaches for AI-Based Trajectory Forecasts of Road Users

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Automated cargo bikes are intended to complement public transportation in a sharing concept and provide an alternative transportation option for people and...

Autonomous Cargo Bike Fleets – Approaches for AI-Based Trajectory Forecasts of Road Users

Publicado en línea: 28 feb 2023

Páginas: 55 - 64

DOI: https://doi.org/10.2478/ttj-2023-0006

Palabras clave
Autonomous driving, Cargo bike, Urban mobility, Trajectory prediction, Neural networks, Deep learning

© 2023 Stefan Sass et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Autonomous Cargo Bike Fleets – Approaches for AI-Based Trajectory Forecasts of Road Users

Publicado en línea: 28 feb 2023

Páginas: 55 - 64

DOI: https://doi.org/10.2478/ttj-2023-0006

Palabras claveAutonomous driving, Cargo bike, Urban mobility, Trajectory prediction, Neural networks, Deep learning

© 2023 Stefan Sass et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
Autonomous driving, Cargo bike, Urban mobility, Trajectory prediction, Neural networks, Deep learning