Autonomous vehicles leverage AI to classify objects around a vehicle, such as pedestrians, crosswalks, and road signs to inform how to position the vehicle, plan a path, and avoid obstacles.
Operationalizing AI for autonomous vehicles presents a series of substantial challenges. These include modeling all the possibilities involved with complex real-world environments, unreliable sensor data from cameras, LIDAR, and RADAR, and real-time decision-making with computationally intensive models.
- Layer real-time monitoring over billions of model predictions to automatically surface the precise features that negatively impact your model performance, and explore areas to improve
- Visually inspect object detection data in an embeddings analyzer to identify edge cases, poor lighting, or noisy images that impact bounding box labels
- Improve models and further mitigate safety concerns with root cause analysis and clear areas to address for active learning and relabeling efforts
Image classification models help autonomous vehicles navigate their environment safely. Image classification models help add context to environmental situations such as weather, lighting, road conditions, and more. Since image classification models utilize unstructured data, the volume and complexity of this data make it tedious to explore and identify images that contribute to performance degradation.
- Inspect image classification data with an embedding visualizer to easily identify bad-quality images, compare training and production environments, and explore anomalous data
- Gain a comprehensive view of all your model data, such as LIDAR/RADAR and object tracking, to narrow in on new patterns and untrained objects.
- Measure embedding drift to quantify and highlight when production data becomes problematic and deviates from expected trends