With the recent emergence of deep learning, computer vision-based applications have demonstrated better applicability in accomplishing driving tasks including drivable road region detection, lane keeping and steering control in self-driving cars. Till recently, numerous lane-marking detection based steering control and lane keeping methods have been proposed to perform autonomous driving on urban well-structured roads. But the matter of fact is that these methods are not feasible on roads where lane markings are not available or faded over time which makes drivable road region detection a crucial task. Moreover, it is highly arduous task to estimate steering angle on deteriorated roads using existing road detection and steering angle estimation methods. To the best of our knowledge, there is no standard benchmark available for drivable road region detection and steering angle estimation on unstructured roads. To this end, we present a large-scale dataset for drivable region road detection, comprising of 15,000-pixel level high quality fine annotations. Alongside dataset, we also present an end-to-end drivable road region detection and steering angle estimation method to ensure the autonomous driving on generalized urban, rural, and unstructured road conditions. The proposed method performs pixel-level segmentation to extract drivable road region and quantifies lane interception to estimate the steering angle of self-driving cars. A comprehensive qualitative and quantitative analysis has been carried out to demonstrate the effectiveness of our proposed dataset, road detection and steering angle estimation methods.
End to End semantic segmentation based drivable road detection and steering angle estimation on unstructured roads for self driving cars
A PyTorch implementation of drivable road region detection and steering angle estimation methods proposed for the autonomous vehicle. The dataset has been taken from CARL-DATASET.
We have extended CARL-Dataset for road detection and segmentation task. As CARL-Dataset has been constructed over video sequences from 100+ cities of Pakistan.
This dataset contains diversities in terms of road types such as
To ensure generalization of our proposed method, equal subsets of images from video sequences of all types of captured roads have been selected for the training and evaluation of proposed method.
Some samples taken from the video sequences of diverse roadways are shown Below.
Some of the pixel-level annotation examples are depicted below.
Download the complete dataset from Here.
My project includes the following files scripts and folders:
python train.py --resume-training no
python test_road_detection.py --model-path <path to saved checkpoint/weight file> --input <path to vid>.
example: python test.py --model-path model.pth --input abc.jpg
python test_vid.py --input <path to vid> --model-path <path to saved checkpoint/weight file>.
example: python test_vid.py --input DSC_0006.mp4 --model-path model.pth
@ARTICLE{9646953,
author={Rasib, Marya and Butt, Muhammad Atif and Riaz, Faisal and Sulaiman, Adel and Akram, Muhammad},
journal={IEEE Access},
title={Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads},
year={2021},
volume={9},
number={},
pages={167855-167867},
doi={10.1109/ACCESS.2021.3134889}}
This code inspired by Sovit Ranjan Rath. Some snippets of steering angle estimation code from David Tain.