A Review of Trajectory Prediction Methods for the Vulnerable Road User
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Robotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-6581/13/1/1 |
| _version_ | 1797342595977838592 |
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| author | Erik Schuetz Fabian B. Flohr |
| author_facet | Erik Schuetz Fabian B. Flohr |
| author_sort | Erik Schuetz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:35:34Z |
| format | Article |
| id | doaj.art-8c06d93602854fb59586d48b2c8b6ea7 |
| institution | Directory Open Access Journal |
| issn | 2218-6581 |
| language | English |
| last_indexed | 2024-03-08T10:35:34Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj.art-8c06d93602854fb59586d48b2c8b6ea72024-01-26T18:21:17ZengMDPI AGRobotics2218-65812023-12-01131110.3390/robotics13010001A Review of Trajectory Prediction Methods for the Vulnerable Road UserErik Schuetz0Fabian B. Flohr1Intelligent Vehicles Lab, Munich University of Applied Sciences, Lothstr. 34, 80335 Munich, GermanyIntelligent Vehicles Lab, Munich University of Applied Sciences, Lothstr. 34, 80335 Munich, GermanyPredicting 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.https://www.mdpi.com/2218-6581/13/1/1surveytrajectory predictionautonomous drivingVRUsmotion forecastingpedestrian trajectory prediction |
| spellingShingle | Erik Schuetz Fabian B. Flohr A Review of Trajectory Prediction Methods for the Vulnerable Road User Robotics survey trajectory prediction autonomous driving VRUs motion forecasting pedestrian trajectory prediction |
| title | A Review of Trajectory Prediction Methods for the Vulnerable Road User |
| title_full | A Review of Trajectory Prediction Methods for the Vulnerable Road User |
| title_fullStr | A Review of Trajectory Prediction Methods for the Vulnerable Road User |
| title_full_unstemmed | A Review of Trajectory Prediction Methods for the Vulnerable Road User |
| title_short | A Review of Trajectory Prediction Methods for the Vulnerable Road User |
| title_sort | review of trajectory prediction methods for the vulnerable road user |
| topic | survey trajectory prediction autonomous driving VRUs motion forecasting pedestrian trajectory prediction |
| url | https://www.mdpi.com/2218-6581/13/1/1 |
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