Exploring Navigation Maps for Learning-Based Motion Prediction

Schmidt, Jochen; Jordan, Julian; Gritschneder, Franz; Monninger, Thomas; Dietmayer, Klaus

doi:10.1109/icra48891.2023.10160989

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…As HD maps are expensive and require regular updates due to environmental changes (e.g., construction sites), alternatives for incorporating static environmental features are sought. Schmidt et al [111] showed that using navigational maps instead of HD maps by training a method with locally available HD maps and navigational maps via a teacher-student scheme is a viable alternative. In datasets like ETH/UCY, which do not offer maps but only images of the surroundings, methods integrate scene information into their trajectory predictions by encoding environment images using a convolutional neural network (CNN)-based backbone [16,26,28,39,79,96].…”

Section: Possible Input Featuresmentioning

confidence: 99%

A Review of Trajectory Prediction Methods for the Vulnerable Road User

Schuetz,

Flohr

2023

Robotics

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Predicting the trajectory of other road users, especially vulnerable road users (VRUs), is an important aspect of safety and planning efficiency for autonomous vehicles. With recent advances in Deep-Learning-based approaches in this field, physics- and classical Machine-Learning-based methods cannot exhibit competitive results compared to the former. Hence, this paper provides an extensive review of recent Deep-Learning-based methods in trajectory prediction for VRUs and autonomous driving in general. We review the state and context representations and architectural insights of selected methods, divided into categories according to their primary prediction scheme. Additionally, we summarize reported results on popular datasets for all methods presented in this review. The results show that conditional variational autoencoders achieve the best overall results on both pedestrian and autonomous driving datasets. Finally, we outline possible future research directions for the field of trajectory prediction in autonomous driving.

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