Handling the Hidden: Occlusion-Aware Motion Prediction for Autonomous Vehicles
Motion prediction plays a vital role in the safe deployment of autonomous vehicles, particularly in highly interactive and dynamic environments. However, challenges such as limited visibility due to obstructions, sensor range limitations, and misdetections can compromise situational awareness and si...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:dnet:digitalcsic_::43bb5a1824295b7a796dfef35b9f19ae |
| Acceso en línea: | http://hdl.handle.net/10261/428537 |
| Access Level: | acceso abierto |
| Palabra clave: | Autonomous vehicles motion prediction occlusion-aware perception system |
| Sumario: | Motion prediction plays a vital role in the safe deployment of autonomous vehicles, particularly in highly interactive and dynamic environments. However, challenges such as limited visibility due to obstructions, sensor range limitations, and misdetections can compromise situational awareness and significantly increase safety risks. This work extends the MultIAMP framework—which integrates a Dynamic Bayesian Network (DBN) and Markov chains to generate multimodal probabilistic predictions—by incorporating enhancements that enable it to handle occlusions using real perception inputs. The proposed approach systematically detects, processes, and anticipates the behavior of occluded areas, enabling safer navigation. The system is quantitatively evaluated using the CARLA simulator in combination with state-of-the-art perception and planning modules. Results, expressed through multiple quantitative metrics across diverse driving experiments, demonstrate that integrating occlusion awareness into the prediction module enhances both comfort and safety for autonomous vehicles. |
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