Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

Future Works ▪

We recommend trying the same framework on other domains [real urban datasets].

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Try with another base model such as Deep lap V3, ProDA ,

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Or with another faster base model architecture, to be replaced the Deep lab version-2 even the model is less accuracy but will be Faster, and to be tested on the both models: baseline, and MTKT.

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Impalement Different Deep Reinforcement learning algorithms such as the Actor-Critic and Game-GAN, and utilize the parallel processing.

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 by mohamed elmesawy - Issuu

Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

Articles inside

References

Future works

Problem Statement

Datasets

Literature and old Trials

Results

Methodology

Introduction

Deployment

Deep Reinforcement Learning – DQN in Carla