Evidential deep learning-based multi-modal environment perception for intelligent vehicles

Geletu, Mihreteab Negash; Giurgi, Danut-Vasile; Josso-Laurain, Thomas; Devanne, Maxime; Wogari, Mengesha Mamo; Lauffenburger, Jean-Philippe

doi:10.1109/iv55152.2023.10186581

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2024

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Cited by 3 publications

References 22 publications

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Conflict Management in a Distance to Prototype-Based Evidential Deep Learning

Geletu,

Giurgi,

Josso-Laurain

et al. 2024

Lecture Notes in Computer Science

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Conflict Management in a Distance to Prototype-Based Evidential Deep Learning

Geletu,

Giurgi,

Josso-Laurain

et al. 2024

Lecture Notes in Computer Science

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Fusion of Semantic Segmentation Models for Vehicle Perception Tasks

Giurgi,

Dezert,

Josso-Laurain

et al. 2024

2024 27th International Conference on Information Fusion (FUSION)

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In self-navigation problems for autonomous vehicles, the variability of environmental conditions, complex scenes with vehicles and pedestrians, and the high-dimensional or real-time nature of tasks make segmentation challenging. Sensor fusion can representatively improve performances. Thus, this work highlights a late fusion concept used for semantic segmentation tasks in such perception systems. It is based on two approaches for merging information coming from two neural networks, one trained for camera data and one for LiDAR frames. The first approach involves fusing probabilities along with calculating partial conflicts and redistributing data. The second technique focuses on making individual decisions based on sources and fusing them later with weighted Shannon entropies. The two segmentation models are trained and evaluated on a particular KITTI semantic dataset. In the realm of multi-class segmentation tasks, the two fusion techniques are compared and evaluated with illustrative examples. Intersection over union metric and quality of decision are computed to assess the performance of each methodology.

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