Abstract
Semantic information provides a valuable source for scene understanding around autonomous vehicles in order to plan their actions and make decisions; however, varying weather conditions reduce the accuracy of the semantic segmentation. We propose a method to adapt to varying weather conditions without supervision, namely without labeled data. We update the parameters of a deep neural network (DNN) model that is
pre-trained on the known weather condition (source domain) to adapt it to the new weather conditions (target domain) without forgetting the segmentation in the known weather condition.
Furthermore, we don’t require the labels from the source domain during adaptation training. The parameters of the DNN are optimized to reduce the distance between the distribution of the features from the images of old and new weather conditions. To measure this distance, we propose three alternatives: W-GAN, GAN and maximum-mean discrepancy (MMD). We evaluate our method on various datasets with varying weather conditions. The results show that the accuracy of the semantic segmentation is improved for varying conditions after adaptation with the proposed method.
Index Terms—Intelligent Transportation Systems; Semantic Scene Understanding; Learning and Adaptive Systems
Read more about the research paper.
This research introduces a novel unsupervised domain adaptation (UDA) approach for semantic segmentation in autonomous vehicles. Our method enables vehicles to accurately interpret their surroundings despite varying weather conditions (rain, fog, etc.) without the need for extensive labeled data.
Challenges of Weather on Autonomous Vehicles:
Traditional semantic segmentation models struggle with weather variations.
Labeling vast amounts of data for different weather conditions is expensive and time-consuming.
Our Solution: Unsupervised Domain Adaptation
We propose a UDA framework that adapts a pre-trained model to new weather conditions without labeled data. This allows for:
Improved Accuracy: Enhanced perception in diverse weather, leading to safer navigation.
Reduced Data Labeling Costs: Less reliance on expensive and time-consuming data labeling.
Codersarts: Accelerate Your Autonomous Vehicle Development
At Codersarts, we specialize in implementing cutting-edge research like this into practical solutions for the automotive industry. We can help you:
Implement the Proposed UDA Framework: Leverage our expertise to translate the research into a functional codebase, integrating it seamlessly into your autonomous vehicle systems.
Customize for Specific Weather Conditions: Tailor the framework to address the unique weather challenges you face in your deployment environment.
Integrate with Existing Systems: Ensure smooth integration of the UDA module with your existing autonomous driving software stack.
Ready to revolutionize autonomous vehicle perception in any weather?
Contact Codersarts today! We offer a free consultation to discuss your specific needs and how our UDA expertise can empower your autonomous vehicles to navigate any condition with confidence.
Additionally:
This research explores three distance measurement techniques for adaptation: W-GAN, GAN, and Maximum Mean Discrepancy (MMD).
The paper demonstrates improved accuracy in semantic segmentation across various weather conditions using the proposed UDA method.
Don't let weather hold back your autonomous vehicle development. Partner with Codersarts to achieve robust and reliable perception!
