CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

Overview

CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

This document describes how to install and use CRISCE (CRItical SCEnario), the tool developed by Jasim Ahmed and others for automatically generate simulations of car crashed from accident sketches using BeamNG.tech.

Repo Organization

.
├── Datasets
│   ├── CIREN
│   └── SYNTH
├── LICENSE
├── PythonRobotics
├── README.md
├── beamngpy-1.18-py2.py3-none-any.whl
├── crisce
└── requirements.txt

The crisce folder contains the source code of the tool. The Datasets folder contains the sample accident sketches compatible with the tool: CIREN contains sketches from NHTSA; SYNT contains synthetic sketches that we manually created from pre-existing car crash simulations in BeamNG.tech.

beamngpy-1.18-py2.py3-none-any.whl is the wheel file necessary to manually install beamngpy the Python API to BeamNG.tech. Tl;DR: The package available on pypi is broken.

requirements.txt lists the python packages needed to install the tool. They are in the usual format accepted by pip.

Dependencies

CRISCE is a tool written in Python, therefore it requires a working Python installation. Specifically, we tested CRISCE with Python 3.7.10

CRISCE uses the BeamNG.tech simulator to run the driving simulations. Therefore, BeamNG.tech must be installed as well.

Note: the version of the simulator used by CRISCE is BeamNG.research v1.7.0.1

BeamNG.tech is free for research use and can be requested to BeamNG.GmbH by submitting the form at the following link: https://register.beamng.tech/

NOTE: BeamNG.tech currently supports only Windows, hence CRISCE cannot be used on other platforms (e.g., Linux/Mac) unless you can resort to full system virtualization. For example, we tested CRISCE using the commercial tool Parallels Desktop running on a Mac Book Pro. Performance will not be the same but at least it gets the job done.

Installation

Installing BeamNG.tech

After successfully registered to https://register.beamng.tech/, you should receive an email with the instructions to access the software and a registration key (i.e., tech.key).

Please download the BeamNG.research v1.7.0.1 and unzip it somewhere in your system.

ATTENTION: BeamNG cannot handle paths with spaces and special characters, so please install it in a location that matches these requirements. We suggest something simple, like C:\BeamNG.research_v1.7.0.1.

We refer to this folder as

Store a copy of the tech.key file in a folder somewhere in your system and rename this copy to research.key. BeamNG use this folder to cache the content and the simulation data.

ATTENTION: BeamNG cannot handle paths with spaces and special characters, so please store the registration file in a location that matches these requirements. We suggest something simple, like C:\BeamNG_user.

We refer to this folder as

Installing CRISCE and its Dependencies

We exemplify the installation and usage of CRISCE using Windows Powershell; you can use other systems (e.g., PyCharm) but in that case you need to adapt the commands below.

Before starting, check that you have installed the right version of Python:

python.exe -V
    Python 3.7.10

To install CRISCE we suggest creating a virtual environment using venv. You can also use conda or similar, but in this case you need to adapt the command below to fit your case.

Move to CRISCE's root folder (i.e., where this file is) and create a new virtual environment:

python.exe -m venv .venv

Activate the virtual environment and upgrade pip, setup tools and wheel.

.venv\Scripts\activate
py.exe -m pip install --upgrade pip
pip install setuptools wheel --upgrade

Install the python dependencies listed in the requirements.txt:

pip install -r requirements.txt

At this point, we need to install beamingly from the provided wheel file:

pip install beamngpy-1.18-py2.py3-none-any.whl

Finally, we need to make sure the code of PythonRobotics is there:

git submodule init
git submodule update

At this point, you should be ready to go.

Confirm that CRISCE is installed using the following command from the root folder of this repo:

py.exe crisce/app.py --help

This command must produce an output similar to:

Usage: app.py [OPTIONS] COMMAND [ARGS]...

Options:
  --log-to PATH  Location of the log file. If not specified logs appear on the
                 console
  --debug        Activate debugging (results in more logging)  [default:
                 (Disabled)]
  --help         Show this message and exit.

Commands:
  generate

Running CRISCE

The current release of CRISCE allows to generate a BeamNG simulation of a car crash from a single sketch using the command generate. This command accepts several parameters that you can list by invoking:

py.exe crisce/app.py generate --help

Usage: app.py generate [OPTIONS]

Options:
  --accident-sketch PATH        Input accident sketch for generating the
                                simulation  [required]
  --dataset-name [CIREN|SYNTH]  Name of the dataset the accident comes from.
                                [required]
  --output-to PATH              Folder to store outputs. It will created if
                                not present. If omitted we use the accident
                                folder.
  --beamng-home PATH            Home folder of the BeamNG.research simulator
                                [required]
  --beamng-user PATH            User folder of the BeamNG.research simulator
                                [required]
  --help                        Show this message and exit.

The following commands show how you can generate a simulation of a real car crash (i.e., from a sketch in the CIREN dataset) and from a simulated crash (i.e., from a sketch in the SYNTH dataset). The difference between the two dataset is that for sketches of real car crashes, we have information about the expected impact; while, for synthetic sketches the information is missing.

For example, to create a simulation form the following sketch (i.e., CIREN-99817): CIREN-99817

CIREN-99817

you can run the following command (after replacing and with the appropriate values:

py.exe crisce/app.py generate generate --accident-sketch .\Datasets\CIREN\99817\ --dataset-name CIREN --beamng-home `
   
    ` --beamng-user 
    

    
   

To create a simulation form the following synthetic sketch (i.e., fourway_1): CIREN-99817

you can run the following command:

py.exe crisce/app.py generate generate --accident-sketch ./Datasets/SYNTH/fourway_1 --dataset-name SYNTH --beamng-home `
   
    ` --beamng-user 
    

    
   

Reporting

The generate command produces a number of intermediate outputs that show the progress of the computation and measure the accuracy of the simulation that is printed on the console:

Quality_of_environment = 33.0, quality_of_crash = 17.0, quality_of_trajecory = 19.009199327937655
Crash Simulation Accuracy =  69.00919932793765 %

The intermediate results instead are stored under the sketch folder (under output) or the folder configured via the --output-to parameter.

For the case CIREN-99817 for example, those are the intermediate results produced by CRISCE:

output/
├── car
│   ├── 0_mask_result_b.jpg
│   ├── 0_mask_result_r.jpg
│   ├── 1_blend_masks_r_b.jpg
│   ├── 1_blend_masks_res.jpg
│   ├── 2_opening_morph.jpg
│   ├── 3_AABB_OBB.jpg
│   ├── 4_crash_point_visualization.jpg
│   ├── 5_triangle_extraction.jpg
│   ├── 6_angles_for_vehicles.jpg
│   ├── 7_sequence_of_movements.jpg
│   ├── 8_twelve_point_model_sides.jpg
│   └── 9_crash_point_on_vehicles.jpg
├── kinematics
│   ├── 0_distorted_control_points.jpg
│   ├── 1_distorted_control_points.jpg
│   ├── 2_distorted_trajectory.jpg
│   ├── 2_original_trajectory.jpg
│   ├── 3_distorted_trajectory.jpg
│   └── 3_original_trajectory.jpg
├── road
│   ├── 0_gray_image.jpg
│   ├── 1_blur_image.jpg
│   ├── 2_threshold_image.jpg
│   ├── 3_dilate_image.jpg
│   ├── 4_erode_image.jpg
│   ├── 5_Contour_Viz_image.jpg
│   ├── 6_midpoints_of_lane.jpg
│   ├── 7_distortion_mapping.jpg
│   └── 8_final_result.jpg
├── simulation
│   ├── 0_sim_plot_road.jpg
│   ├── 1_sim_initial_pos_dir.jpg
│   ├── 2_sim_bbox_traj.jpg
│   ├── 3_crisce_beamng_efficiency.jpg
│   ├── 3_crisce_efficiency.jpg
│   └── 4_trace_veh_BBOX.jpg
└── summary.json
Owner
Chair of Software Engineering II, Uni Passau
Chair of Software Engineering II, Uni Passau
AutoDeeplab / auto-deeplab / AutoML for semantic segmentation, implemented in Pytorch

AutoML for Image Semantic Segmentation Currently this repo contains the only working open-source implementation of Auto-Deeplab which, by the way out-

AI Necromancer 299 Dec 17, 2022
A framework for joint super-resolution and image synthesis, without requiring real training data

SynthSR This repository contains code to train a Convolutional Neural Network (CNN) for Super-resolution (SR), or joint SR and data synthesis. The met

83 Jan 01, 2023
This is an open source library implementing hyperbox-based machine learning algorithms

hyperbox-brain is a Python open source toolbox implementing hyperbox-based machine learning algorithms built on top of scikit-learn and is distributed

Complex Adaptive Systems (CAS) Lab - University of Technology Sydney 21 Dec 14, 2022
Easy and comprehensive assessment of predictive power, with support for neuroimaging features

Documentation: https://raamana.github.io/neuropredict/ News As of v0.6, neuropredict now supports regression applications i.e. predicting continuous t

Pradeep Reddy Raamana 93 Nov 29, 2022
Deep Learning pipeline for motor-imagery classification.

BCI-ToolBox 1. Introduction BCI-ToolBox is deep learning pipeline for motor-imagery classification. This repo contains five models: ShallowConvNet, De

DongHee 18 Oct 31, 2022
[IEEE Transactions on Computational Imaging] Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting

Few-shot Deep HDR Deghosting This repository contains code and pretrained models for our paper: Self-Gated Memory Recurrent Network for Efficient Scal

Susmit Agrawal 4 Dec 29, 2021
Progressive Coordinate Transforms for Monocular 3D Object Detection

Progressive Coordinate Transforms for Monocular 3D Object Detection This repository is the official implementation of PCT. Introduction In this paper,

58 Nov 06, 2022
PyTorch implementation(s) of various ResNet models from Twitch streams.

pytorch-resnet-twitch PyTorch implementation(s) of various ResNet models from Twitch streams. Status: ResNet50 currently not working. Will update in n

Daniel Bourke 3 Jan 11, 2022
Python scripts form performing stereo depth estimation using the CoEx model in ONNX.

ONNX-CoEx-Stereo-Depth-estimation Python scripts form performing stereo depth estimation using the CoEx model in ONNX. Stereo depth estimation on the

Ibai Gorordo 8 Dec 29, 2022
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight

Revisiting RCAN: Improved Training for Image Super-Resolution Introduction Image super-resolution (SR) is a fast-moving field with novel architectures

Zudi Lin 76 Dec 01, 2022
Face and Body Tracking for VRM 3D models on the web.

Kalidoface 3D - Face and Full-Body tracking for Vtubing on the web! A sequal to Kalidoface which supports Live2D avatars, Kalidoface 3D is a web app t

Rich 257 Jan 02, 2023
Breaching - Breaching privacy in federated learning scenarios for vision and text

Breaching - A Framework for Attacks against Privacy in Federated Learning This P

Jonas Geiping 139 Jan 03, 2023
Using fully convolutional networks for semantic segmentation with caffe for the cityscapes dataset

Using fully convolutional networks for semantic segmentation (Shelhamer et al.) with caffe for the cityscapes dataset How to get started Download the

Simon Guist 27 Jun 06, 2022
This is an official repository of CLGo: Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints

CLGo This is an official repository of CLGo: Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints An earlier

刘芮金 32 Dec 20, 2022
A Player for Kanye West's Stem Player. Sort of an emulator.

Stem Player Player Stem Player Player Usage Download the latest release here Optional: install ffmpeg, instructions here NOTE: DOES NOT ENABLE DOWNLOA

119 Dec 28, 2022
Air Pollution Prediction System using Linear Regression and ANN

AirPollution Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living Publication Link:

Dr Sharnil Pandya, Associate Professor, Symbiosis International University 19 Feb 07, 2022
Underwater industrial application yolov5m6

This project wins the intelligent algorithm contest finalist award and stands out from over 2000teams in China Underwater Robot Professional Contest, entering the final of China Underwater Robot Prof

8 Nov 09, 2022
Code for BMVC2021 paper "Boundary Guided Context Aggregation for Semantic Segmentation"

Boundary-Guided-Context-Aggregation Boundary Guided Context Aggregation for Semantic Segmentation Haoxiang Ma, Hongyu Yang, Di Huang In BMVC'2021 Pape

Haoxiang Ma 31 Jan 08, 2023
Revisiting Weakly Supervised Pre-Training of Visual Perception Models

SWAG: Supervised Weakly from hashtAGs This repository contains SWAG models from the paper Revisiting Weakly Supervised Pre-Training of Visual Percepti

Meta Research 134 Jan 05, 2023
Official implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

CrossViT This repository is the official implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification. ArXiv If

International Business Machines 168 Dec 29, 2022