Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

Methodology we used deeplap-v2 as our model it is built upon the resnet -101 backbone initialized with ImageNet pre-trained weights, conducted with Pytorch, the deep Lab pipeline as it appears in figure 8-a .

DEEP LAP V2

Fig8-a: Deeplap V2 Pipeline

It uses ResNet-101: ▪ ▪ ▪

Atrous Convolution. Atrous Spatial Pyramid Pooling (ASPP) – [New in V2]. Fully Connected Conditional Random Field (CRF).

Fig8-b - explain the backbone of deep lab version-2 model.

Urban Scene Segmentation for Autonomous Vehicles Using Multi-Domain Adaptation

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Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

Published on Feb 4, 2022

mohamed elmesawy

The project tackles the problem of multi-target domains/contexts for the self-driving car in urban cities, using the unsupervised domain adaptation (UDA) methodology. and compares two approaches: 1- `Baseline Model`: which deals with the problem of the multi targets domain as if it is a single target. 2- `MTKT Model`, which consists of multiple teachers [one for each target domain] that distill their knowledge to a single student which will be used as the main model in the inference time. Our project built on the latest SOTA paper of #Valeo France https://arxiv.org/abs/2108.06962 Also, we have developed a self-driving car agent implementing the Deep #Reinforcement algorithm #DQN inside #Carla Simulator.

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Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

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References

Future works

Problem Statement

Datasets

Literature and old Trials

Results

Methodology

Introduction

Deployment

Deep Reinforcement Learning – DQN in Carla