Using CNN to mimic the driver based on training data from Torcs

Last update: Jan 05, 2022

Overview

Behavioural-Cloning-in-autonomous-driving

Using CNN to mimic the driver based on training data from Torcs.

Approach

First, the data was collected from the game while i played using collecting_data.py script. Images of the game window and the keys i pressed are simultaneously recorded and stored in a dataframe('trainingdatareg.csv') in the path declared there. Then the data is cleaned by balancing it's proportion, removed unwanted columns and then cropped out the ROI for feeding it into the model using datacleaning.py script Trained various model using transfer learning like DenseNet, VGG19, EfficientNet etc and chose the model with high accuracy ('DenseNet') with the help of kaggle GPU and saved it. Then tested the model.

Result

As of now the model detects lane and adjust it's input to it and performs well in a straight lane but it gets cranky while turning. Need to adjust it later.

Reference:

Sentdex:https://www.youtube.com/c/sentdex

https://arxiv.org/abs/1910.06734

https://towardsdatascience.com/behavioural-cloning-applied-to-self-driving-car-on-a-simulated-track-5365e1082230

Using CNN to mimic the driver based on training data from Torcs

Related tags

Overview

Behavioural-Cloning-in-autonomous-driving

Approach

Result

Reference:

Owner

Sudharshan

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Using CNN to mimic the driver based on training data from Torcs

Related tags

Overview

Behavioural-Cloning-in-autonomous-driving

Approach

Result

Reference:

Owner

Sudharshan

Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness

Code for Towards Streaming Perception (ECCV 2020) :car:

Anonymize BLM Protest Images

[CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation

CVPR 2020 oral paper: Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax.

This is a template for the Non-autoregressive Deep Learning-Based TTS model (in PyTorch).

Official code for the paper: Deep Graph Matching under Quadratic Constraint (CVPR 2021)

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Multi Task Vision and Language

Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

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This is a Tensorflow implementation of Learning to See in the Dark in CVPR 2018

Code for project: "Learning to Minimize Remainder in Supervised Learning".

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Pytorch code for "DPFM: Deep Partial Functional Maps" - 3DV 2021 (Oral)

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