M2P3
| Autor: | Christian Müller, Igor Vozniak, Atanas Poibrenski, Matthias Klusch |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Pedestrian Autoencoder Recurrent neural network Position (vector) 020204 information systems Path (graph theory) 0202 electrical engineering electronic engineering information engineering RGB color model Computer vision Artificial intelligence Set (psychology) business |
| Zdroj: | SAC |
| Popis: | Accurate prediction of the future position of pedestrians in traffic scenarios is required for safe navigation of an autonomous vehicle but remains a challenge. This concerns, in particular, the effective and efficient multimodal prediction of most likely trajectories of tracked pedestrians from egocentric view of self-driving car. In this paper, we present a novel solution, named M2P3, which combines a conditional variational autoencoder with recurrent neural network encoder-decoder architecture in order to predict a set of possible future locations of each pedestrian in a traffic scene. The M2P3 system uses a sequence of RGB images delivered through an internal vehicle-mounted camera for egocentric vision. It takes as an input only two modes, that are past trajectories and scales of pedestrians, and delivers as an output the three most likely paths for each tracked pedestrian. Experimental evaluation of the proposed architecture on the JAAD and ETH/UCY datasets reveal that the M2P3 system is significantly superior to selected state-of-the-art solutions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|