
2023.06.01
In this study, we propose a method for potential risk estimation of road scenes from driving videos and investigate the relationship between the potential risk estimation and the risk perception of humans. We employ a frame prediction method and define scenes where the frame prediction accuracy decreases as risky scenes. We also use the scene depth estimated from the color image and use the prediction error of the scene depth as another risk criteria. The relationship between the proposed risk criteria and the risk perception of humans was evaluated by subject experiments.
Papers
- "Road Scene Risk Estimation using Driving Video", Masafumi Kishimoto, Masaaki Iiyama, 25th International Conference on Human-Computer Interaction (HCII2023), Lecture Notes in Computer Science book series (LNCS,volume 14023), pp 397–406, 2023-07. DOI: 10.1007/978-3-031-35939-2_29 https://link.springer.com/chapter/10.1007/978-3-031-35939-2_29
