Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

Reinforcement learning in our project Deep Reinforcement Learning – DQN in Carla: ▪

In deep Q-learning, we use a Neural Network to approximate the Q-value function, which is called a Deep Q Network [DQN].

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This function maps a state [Input] to the Q-values of all the actions that can be taken from that state [Output].

Fig22-a: Deep Q-Learning

EQN 2: Loss Function in Deep Q-Learning

DQN Algorithm:

Fig23-a: DQN Architecture

Fig23-b: Sample of the Set of states, actions, and rewards

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