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| en:safeav:maps:ai [2025/10/21 16:15] – [Deep Learning Architectures] kosnark | en:safeav:maps:ai [2026/04/24 09:43] (current) – raivo.sell | ||
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| ====== AI-based Perception and Scene Understanding ====== | ====== AI-based Perception and Scene Understanding ====== | ||
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| - | <todo @bertlluk # | ||
| Advances in AI, especially the convolutional neural network, allow us to process raw sensory information and recognize objects and categorize them into classes with higher levels of abstraction (pedestrians, | Advances in AI, especially the convolutional neural network, allow us to process raw sensory information and recognize objects and categorize them into classes with higher levels of abstraction (pedestrians, | ||
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| Alternatively, | Alternatively, | ||
| These models are critical for estimating the shape and distance of objects in 3D space, especially under challenging lighting or weather conditions. | These models are critical for estimating the shape and distance of objects in 3D space, especially under challenging lighting or weather conditions. | ||
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| === Transformer Architectures === | === Transformer Architectures === | ||
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| Notable examples include '' | Notable examples include '' | ||
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| === Recurrent and Temporal Models === | === Recurrent and Temporal Models === | ||
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| More recent architectures use temporal convolutional networks or transformers to achieve similar results with greater parallelism and stability. | More recent architectures use temporal convolutional networks or transformers to achieve similar results with greater parallelism and stability. | ||
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| === Graph Neural Networks (GNNs) === | === Graph Neural Networks (GNNs) === | ||
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| Robust perception requires exposure to the full range of operating conditions that a vehicle may encounter. | Robust perception requires exposure to the full range of operating conditions that a vehicle may encounter. | ||
| Datasets must include variations in: | Datasets must include variations in: | ||
| - | * **Sensor modalities** – data from cameras, LiDAR, radar, GNSS, and IMU, reflecting the multimodal nature of perception. | + | |
| - | * **Environmental conditions** – daytime and nighttime scenes, different seasons, weather effects such as rain, fog, or snow. | + | |
| - | * **Geographical and cultural contexts** – urban, suburban, and rural areas; diverse traffic rules and road signage conventions. | + | * **Environmental conditions** – daytime and nighttime scenes, different seasons, weather effects such as rain, fog, or snow. |
| - | * **Behavioral diversity** – normal driving, aggressive maneuvers, and rare events such as jaywalking or emergency stops. | + | * **Geographical and cultural contexts** – urban, suburban, and rural areas; diverse traffic rules and road signage conventions. |
| - | * **Edge cases** – rare but safety-critical situations, including near-collisions or sensor occlusions. | + | * **Behavioral diversity** – normal driving, aggressive maneuvers, and rare events such as jaywalking or emergency stops. |
| + | * **Edge cases** – rare but safety-critical situations, including near-collisions or sensor occlusions. | ||
| A balanced dataset should capture both common and unusual situations to ensure that perception models generalize safely beyond the training distribution. | A balanced dataset should capture both common and unusual situations to ensure that perception models generalize safely beyond the training distribution. | ||
