DDD20 是一个非常棒是数据集，DDD20 的 readme 文件原网页无法打开，为方便国内研究，本博客将其转载。

【转载】https://docs.google.com/document/d/1Nnyjo4j0rvdgHQ0cS8z0Q1QBRLHDtwh7XW1u9ygmQEs/edit#

DAVIS Driving Dataset 2020 (DDD20)

Yuhuang Hu, Jonathan Binas, Daniel Neil, Shih-Chii Liu, Tobi Delbruck

March 2020

Web: This document | analytics

Link to source document

DDD20 (DDD20 website) is an expanded release of the first public end-to-end training dataset of automotive driving using an inilabs DAVIS event+frame camera that was developed in the Sensors Group of the Inst. of Neuroinformatics, UZH-ETH Zurich.

DDD20 includes car data such as steering, throttle, GPS, etc. It can be used to evaluate the fusion of frame and event data for automobile driving applications.

See more Inst. of Neuroinformatics Sensors Group datasets here.

Change history

Feb 2020: retitled to DDD20, adjusted layout, added slides, replaced header image with heatmap of driving locations
Sept 2018: added folders for lens calibration and camera settings
Jun 2018: added RAL submission, added get_stats script usage
Nov 2017: added Ford Focus dataset to original DDD17
Sept 4, 2017: added wasabi download option, added DAVIS description
Aug 19, 2017, added more tips on recording
June 25, 2017 added more detailed dataset description
June 21, 2017 added initial info on recording new data
June 5, 2017 created

Change history

Table of contents

Citing DDD20

Samples of DDD20 videos

Contact

Getting the dataset

1. Using Resilio Sync

2. Download via a set of URLs from wasabi cloud storage

Getting ddd20-utils software

Branches

Dataset contents

DDD20 dataset files spreadsheet

Maps

DAVIS camera

Camera settings

Lens calibration

cameraMatrix.xml

distortionCoef.xml

Vehicle data

Folder structure

Preliminary jAER aedat recordings

HDF5 file contents

Using HDFView to see structure of data files

Data structures in HDF5 files

ddd20-utils usage

Note for python newbies regarding PATH

Extra python libraries needed

view.py: Get help for the viewer

view.py: Play a file from the beginning

Example

Play a file, starting at X percent

Play a file starting at second X

Rotate the scene during the playback

Controlling view.py

Troubleshooting

get_stats.py: Export statistics in a file

export.py: Export data for training a network (reduce data to frame-based representation):

Example: default export

Example: make HDF5 file with 10k event frames

Example networks and tools

Troubleshooting

Data Visualization.ipynb

Steering wheel prediction

Recording new data

Requirements

Hardware

Software

Firmware

Recording checklist

Usage record.py

Troubleshooting

Sensor alignment

APS exposure control

Detecting corrupted recordings

Useful aliases for recording

Useful commands

Citing DDD20

The main DDD20 citation is

Hu, Y., Binas, J., Neil, D., Liu, S.-C., and Delbruck, T. (2018). "DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction". Special session Beyond Traditional Sensing for Intelligent Transportation, The 23rd IEEE International Conference on Intelligent Transportation Systems, September 20 – 23, 2020, Rhodes, Greece. arXiv [cs.CV]. arXiv. http://arxiv.org/abs/2005.08605

Previous publications include

1. Binas, J., Neil, D., Liu, S.-C., and Delbruck, T. (2017). DDD17: End-To-End DAVIS Driving Dataset. arXiv:1711.01458 [cs]. Available at: http://arxiv.org/abs/1711.01458 . (available online)
2. Binas, J., Neil, D., Liu, S.-C., and Delbruck, T. (2017). DDD17: End-To-End DAVIS Driving Dataset. in ICML’17 Workshop on Machine Learning for Autonomous Vehicles (MLAV 2017) (Sydney, Australia).
  Available at: https://openreview.net/forum?id=HkehpKVG-&noteId=HkehpKVG- .(only available via openreviews)
  ICML Workshop page: https://sites.google.com/site/ml4autovehicles2017/home
The sensor used for DDD17 is the DAVIS based on the original paper below (about a previous generation sensor IC)
1. Berner, Raphael, Christian Brandli, Minhao Yang, Shih-Chii Liu, and Tobi Delbruck. 2014. “A 240x180 10mW 12us Latency Sparse-Output Vision Sensor for Mobile Applications” In IEEE J. Solid State Circuits. 49(10) p. 2333-2341 10.1109/JSSC.2014.2342715 . Get PDF here.

The DAVIS is based on a seminal DVS paper

Lichtsteiner, Patrick, Christoph Posch, and Tobi Delbruck. 2008. “A 128 X 128 120 dB 15 Μs Latency Asynchronous Temporal Contrast Vision Sensor .” IEEE Journal of Solid-State Circuits 43 (2): p. 566–576. doi:10.1109/JSSC.2007.914337. Get PDF here.

Link to DDD20 samples.mp4.

Screen shot of view.py output from one DDD17 recording

<iframe width="560" height="315" src="https://www.youtube.com/embed/L_CFgfgsn7I" frameborder="0" allowfullscreen></iframe>

https://youtu.be/L_CFgfgsn7I

Samples of DDD20 videos

Contact

For questions related to the use of this dataset or the associated tools, please write yuhuang.hu@ini.uzh.ch or tobi@ini.uzh.ch.

Getting the dataset

The complete DDD20 dataset consists of two parts, the original DDD17 part and the additional DDD20 part.

There are 2 methods to get the data

Use Resilio Sync (a private bittorrent protocol) to get the data. Using this method spreads the bandwidth and doesn’t cost us anything.
Contact us if ResilioSync method is not possible. Download via a set of URLs from wasabi cloud storage. Each download costs us about $50 so we will disable this method if it exceeds our budget. We appreciate use of the bittorrent method above.
md5 checksums of all the files are available in this file.

1. Using Resilio Sync

Use Resilio Sync to get the DDD17/DDD20 datasets,

DDD17: Resilio Sync DDD17 dataset.
DDD20: Resilio Sync DDD20 dataset

Clicking on either link above will pop up a result like this:

Resilio Sync link result

On Windows, clicking the “I already have Sync” produces this result:

Selecting “Open Resilio Sync” results in this dialog:

Once you install and run Resilio Sync, you can select the option to “Selective Sync” to synchronize only part of the data.

On linux, you should copy the link above, and paste into the Resilio web GUI interface for sync by selecting the + button and selecting the “Enter a key or link” item.

Note: for linux firefox users, see this post for help to access the data, since firefox will not recognize the btsync protocol.

Site hosting this and other data: http://sensors.ini.uzh.ch/databases.html

2. Download via a set of URLs from wasabi cloud storage

Each download of the DDD17/DDD20 dataset costs us about $50 so we have disabled this method.

Getting ddd20-utils software

We provide the ddd20-utils python software for recording, viewing, and exporting the data. Code and further instructions are available at https://github.com/SensorsINI/ddd20-utils. Clone it with

git clone https://github.com/SensorsINI/ddd20-utils

See the README.md for python library dependencies (opencv-python and h5py).

Useful command line

pip install opencv-contrib-python h5py

Branches

master branch is the main one.

See ddd20-utils usage for further information.

Dataset contents

DDD20 dataset files spreadsheet

The spreadsheet “DVS Driving Dataset 2017 (DDD20) description” describes each recording (see next section).

Maps

Maps of each recording route recorded by GPS waypoints (when available) are in DDD17plus-maps.zip.. Map links to HTML hosted on our institute website (google docs does not serve raw HTML) are included in the DDD20 file descriptions spreadsheet. Maps use our Google maps API key under our “argo” project.

The USA recordings cover this heat map. The HTML of this heat map is in our DDD20 gdrive and can also be accessed via our institute website here.

DAVIS camera

The cameras used for these recordings are advanced (at time of recording) 346x260 pixel DAVIS vision sensors. The DAVIS outputs conventional global shutter gray scale image frames and dynamic vision sensor (DVS) events. The DAVIS also includes its own inertial measurement unit (IMU) that measures camera rotation and acceleration.

Camera settings

Typical values for camera settings used in these recordings are stored in the folder fordFocus/camerasettings:

This caer-config.xml file is the file that should be placed in the caer startup folder to set bias currents and all other camera parameters.

Lens calibration

The camera body used for the recordings melted during a hot day and had to be jury-rigged back together. After the completion of recordings the body was replaced by a metal body and the original case was discarded. To provide a camera calibration that is indicative of the actual calibration, we recorded calibration data using the same Kowa 4.5mm C-mount lens type using the new body. This camera calibration data is stored in the folder fordfocus/lenscalibration:

Calibration was performed using the jAER SingleCameraCalibration class

The human readable calibration data is

cameraMatrix and distortionCoefs are in opencv xml format:

cameraMatrix.xml

<?xml version="1.0"?>

<opencv_storage>

<cameraMatrix type_id="opencv-matrix">

<rows>3</rows>

<cols>3</cols>

<dt>d</dt>

<data>

2.5075316722003879e+002 0. 1.8456304977790828e+002 0.

2.5078290094673741e+002 1.1789773837487046e+002 0. 0. 1.</data></cameraMatrix>

</opencv_storage>

distortionCoef.xml

<?xml version="1.0"?>

<opencv_storage>

<distortionCoefs type_id="opencv-matrix">

<rows>1</rows>

<cols>5</cols>

<dt>d</dt>

<data>

-3.4983978901537061e-001 2.2939425998194787e-001

1.5441863394185178e-004 6.8956256994944149e-004

-1.0553679782358873e-001</data></distortionCoefs>

</opencv_storage>

Vehicle data

The following vehicle-related variables have been recorded together with the visual DAVIS data.

Variable name	Range	Units	Frequency (approx.)
steering_wheel_angle	-600 to +600	degrees	max 10 Hz
torque_at_transmission	-500 to 1500	Nm	max 10 Hz
engine_speed	0 to 16382	RPM	max 10 Hz
vehicle_speed	0 to 655	km/h	max 10 Hz
accelerator_pedal_position	0 to 100	%	max 10 Hz
parking_brake_status	boolean (1 = engaged)		1Hz and immediately on change
brake_pedal_status	boolean (1 = pressed)		1Hz and immediately on change
transmission_gear_position	States: -1, 0, 1, 2, 3, 4, 5, 6, 7, 8		1Hz and immediately on change
odometer	0 to 16777214	km	max 10 Hz
ignition_status	0, 1, 2, 3		1Hz and immediately on change
fuel_level	0 to 100	%	max 2 Hz
fuel_consumed_since_restart	0 to 4294967295.0	L	max 10 Hz
headlamp_status	boolean (1 = on)		1Hz and immediately on change
high_beam_status	boolean (1 = on)		1Hz and immediately on change
windshield_wiper_status	boolean (1 = on)		1Hz and immediately on change
latitude	-89 to 89	degrees	max 1 Hz
longitude	-179 to 179	degrees	max 1 Hz

The dataset is divided into various recording files, generated under different weather, driving, and street conditions.

There are some slightly tricky aspects to the data fields. The Mondeo and Focus have different automatic transmissions. For example the Mondeo has additional settings related to downhill mode. See OpenXC doc for firmware types, car models, and available data types.

Folder structure

The overall folder structures are shown below.

DDD17/DDD20 folder structures

Preliminary jAER aedat recordings

Several preliminary recordings in DDD17 run1_test were done using jAER and these recordings are supplied in AER-DAT2.0 format as aedat files. The associated OpenXC data are supplied as .dat files. These preliminary recordings can be played in jAER’s AEViewer using the AEChip class eu.seebetter.ini.chips.davis.Davis346B and the EventFilter ch.unizh.ini.jaer.projects.e2edriving.FordVIVisualizer. A screenshot is shown below. The aedat file should be opened after the .dat file is loaded using the LoadFordVIDataFile button.

Screen shot showing 80ms DVS frame and associated OpenXC data from the recording “Davis346B-2016-12-15T11-45-08+0100-00INX006-0 to airport.aedat” with OpenXC data file “FordVI-2016-12-15-rec01-to-airport-and-back.dat”

HDF5 file contents

Using HDFView to see structure of data files

We recommend HDFView for exploring the file container structure. Here is the steering wheel data for one file.

Note: attempting to explore the dvs data throws an exception in HDFView, but this is expected from the variable length cAER data that is contained in the dvs container.

HDFView inspecting steering wheel angle (Ford Mondeo) and Vehicle speed (Ford Focus) data for one recording

Data structures in HDF5 files

Data structure is defined by record.py code that writes HDF files using caer.py methods for packing the DAVIS data to HDF arrays.

The code fragment that stores a packet of DAVIS data is

def save_aer(ds, data):
''' send aer data dict to dataset buffer '''
row = [ np.fromstring(np.bytes_(data['dvs_timestamp']), dtype=np.uint8),
np.fromstring(data['dvs_header'], dtype=np.uint8),
np.fromstring(data['dvs_data'], dtype=np.uint8), ]
ds.save({'dvs_timestamp': data['dvs_timestamp'], 'dvs_data': row})

The rows are indexed by the DAVIS camera timestamp in seconds (dvs_timestamp). The individual DVS events and IMU samples and APS frames have their own camera-local timestamp in hardware int32 us from arbitrary (power on or timestamp reset) starting point.

Each row has the packet timestamp (again), the packet header, and the data payload.

Car data like steering_wheel_angle shown above are straightforward tables of rows of sensor readings, each with a timestamp in double seconds in the first column and data value in the second column. Data is saved with this code fragment

ds.save({
data['name'] + '_data': [data['timestamp'], val],
data['name'] + '_timestamp': data['timestamp']
})

The additional timestamp vector with each topic (e.g. steering_wheel_angle/timestamp or dvs/timestamp) is common for each data type and is the timestamp in long us since 1970.

The dvs data has all the DAVIS camera data, packed in (awkward) variable length containers that each contain either DVS, APS, or IMU data. Each packet has only one type of data. This dvs data is extracted by the caer.py library. See export.py below for more information.

ddd20-utils usage

The following examples are from a terminal/console command prompt.

Note for python newbies regarding PATH

ddd20-utils should be on the PATH to enable python to find the scripts like view.py. It should also be on PYTHONPATH so that the scripts can import the libraries. Examples from .bashrc

export PATH="~/git/ddd20-utils:~/bin:~/jdk1.8.0_131/jre/bin:$PATH"
export PYTHONPATH=$PYTHONPATH:~/git/ddd20-utils

If the script is on the PATH, and it is executable, then a leading python is not needed to launch the script.

Extra python libraries needed

The following may need to be run

pip install openxc h5py opencv-python numpy

view.py: Get help for the viewer

$ view.py --help
usage: view.py [-h] [--start START] [--rotate ROTATE] filename

positional arguments:
filename

optional arguments:
-h, --help show this help message and exit
--start START, -s START
Examples:
-s 50% - play file starting at 50%
-s 66s - play file starting at 66s
--rotate ROTATE, -r ROTATE
Rotate the scene 180 degrees if True, Otherwise False

view.py: Play a file from the beginning

$ view.py <recorded_file.hdf5>

Example

Assumes data is stored in /media/driving

$ view.py /media/driving/run3/rec1487355025.hdf5

Play a file, starting at X percent

$ view.py <recorded_file.hdf5> -s X%

Play a file starting at second X

$ view.py <recorded_file.hdf5> -s Xs

Rotate the scene during the playback

There is an option rotate180 in view.py that applies a 180 degree rotation to the APS and DVS displays. Set it true like this:

$ view.py -r True <recorded_file.hdf5>

Controlling view.py

Left click mouse on the timeline window to select a new time.
Right click to bring up opencv menu and freeze playback
Mouse wheel zooms image on all opencv windows
Key i toggles car info display
Keys f and s play faster / slower by 20% (decrease or increase DVS frame duration)
Key b and d brighten /darken the DVS image by decreasing / increasing the full scale event count

Troubleshooting

If you get a gtk error when trying to run view.py, then you may need to use a different opencv or download/install opencv yourself, since pip install opencv-python may not install a working opencv. One instance of downloading/building/installing is

wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.1.0.zip

unzip opencv.zip

cd opencv-3.1.0/

mkdir build

cd build/

cmake -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON ..

make -j 4

make install

If you get the following error:

$ python view.py

Traceback (most recent call last):

File "view.py", line 21, in <module>

import numpy as np

ImportError: No module named numpy

You need to install numpy. Do pip install numpy

If you get the following error:

tobi@tobi-dh87:~/ddd20-utils$ ./view.py

/home/tobi/anaconda/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.

from ._conv import register_converters as _register_converters

Traceback (most recent call last):

File "./view.py", line 28, in <module>

from interfaces.caer import DVS_SHAPE, unpack_header, unpack_data

File "/home/tobi/ddd20-utils/interfaces/__init__.py", line 3, in <module>

import oxc as openxc

File "/home/tobi/ddd17-utils/interfaces/oxc.py", line 15, in <module>

from openxc.tools import dump as oxc

ImportError: No module named openxc.tools

then install the openxc libraries to access the car data.

get_stats.py: Export statistics in a file

To output statistics for a particular variable, try this:

$ python get_stats.py attribute_name recording_name_1.hdf5 recording_name_2.hdf ...

The attribute name can be one of the following attributes:

'steering_wheel_angle',
'brake_pedal_status',
'accelerator_pedal_position',
'engine_speed',
'vehicle_speed',
'windshield_wiper_status',
'headlamp_status',
'transmission_gear_position',
'torque_at_transmission',
'fuel_level',
'high_beam_status',
'ignition_status',
'latitude',
'longitude',
'odometer',
'parking_brake_status',
'fuel_consumed_since_restart'

export.py: Export data for training a network (reduce data to frame-based representation):

export.py creates another hdf5 file, containing the frame-based data.

$ export.py [-h] [--tstart TSTART] [--tstop TSTOP] [--binsize BINSIZE]
[--update_prog_every UPDATE_PROG_EVERY]
[--export_aps EXPORT_APS] [--export_dvs EXPORT_DVS]
[--out_file OUT_FILE]
filename

All times are in seconds.
If BINSIZE is positive, events will be binned into time-slices of BINSIZE seconds (typical values are tens of milliseconds, e.g. 0.050 for 50 ms). Default binsize is 0.1 seconds.
If BINSIZE is a negative integer, frames will be generated every abs(BINSIZE) events (e.g. BINSIZE = -5000 for a constant event count of 5000).
EXPORT_APS and EXPORT_DVS are integer values, 1 for true (default is 1). They determine if DVS and/or APS frames are generated.
UPDATE_PROG_EVERY is the progress bar update interval (in percentage points), default is 0.01.
If -out_file is not supplied, then it takes the original filename and adds _export (i.e. rec01.hdf5 -> rec01_export.hdf5)
The combinations of options can result in the following kind of DVS and APS frame output:

Example: default export

Export a file with default settings

$ cd /media/sf_ddd17/run3
$ export.py rec1487417411.hdf5
accelerator_pedal_position final block: 161
parking_brake_status final block: 32
.....
[DEBUG] sys_ts/tstop 1487419509.07 1487419509.07

closed output file
[DEBUG] output done
[DEBUG] merger done
[DEBUG] stream joined
[DEBUG] merger joined
Finished. Wrote 1443.0MiB to rec1487417411_export.hdf5.

Example: make HDF5 file with 10k event frames

$ export.py --binsize -10000 --update_prog_every 10 --export_aps 0 --out_file /media/driving/run3/dvs1487355025.hdf5 /media/driving/run3/rec1487355025.hdf5
accelerator_pedal_position final block: 3
parking_brake_status final block: 0
...
searching parking_brake_status 1487355026840441
...
closed input file
...
Finished. Wrote 168.0MiB to /media/driving/run3/dvs1487355025.hdf5.

Figure: Different exporting modes explained. The mode (fixed time interval or fixed event count) is selected by positive (time interval in seconds) or negative (number of events) --binsize argument, respectively.

The output dvs_frame is a 3-tensor, with number_of_frames x width x height elements.

Example networks and tools

You should be able to access dvs_frames with h5py, and you can call .dtype on it to determine datatype or .size to get its multidimensional shape.

Before you call export, however, there won't be a dvs_frames entry.

We recommend looking at the ddd17-utils ipython notebooks Data Visualization.ipynb and Model Evaluation and Movie Render.ipynb. These have an example of how to to view the data and render the movies.

To start the notebook, type “ipython notebook” to a terminal. You can pass in the notebook name as the third argument.

Data Visualization.ipynb walks the user through loading the data and visualizing it
Model Evaluation and Movie Creation loads a trained network, plots the results, and renders one of those driving movies using whatever network is supplied
if you want to see how it’s done, check process_data.sh, which calls export, then preprocesses the data to resize it to something reasonable, then trains and tests a network on whatever datafiles are listed in the work list
The latest version supports multiple hdf5 input files, Check multiprocess_data.sh to see the script for running all the data from day 5.

Troubleshooting

$ ipython notebook

Could not start notebook. Please install ipython-notebook

$ pip install ipython-notebook

Collecting ipython-notebook

Could not find a version that satisfies the requirement ipython-notebook (from versions: )

No matching distribution found for ipython-notebook

$ sudo pip install --upgrade ipython[notebook]

Collecting ipython[notebook]

Downloading ipython-5.5.0-py2-none-any.whl (758kB)

100% |████████████████████████████████| 768kB 950kB/s

Collecting pickleshare (from ipython[notebook])

…..

Successfully uninstalled ipython-2.4.1

Successfully installed backports.shutil-get-terminal-size-1.0.0 bleach-2.1.1 certifi-2017.7.27.1 decorator-4.1.2 entrypoints-0.2.3 functools32-3.2.3.post2 html5lib-1.0b10 ipykernel-4.6.1 ipython-5.5.0 ipython-genutils-0.2.0 ipywidgets-7.0.3 jsonschema-2.6.0 jupyter-client-5.1.0 jupyter-core-4.3.0 mistune-0.7.4 nbconvert-5.3.1 nbformat-4.4.0 notebook-5.2.0 pandocfilters-1.4.2 pathlib2-2.3.0 pexpect-4.2.1 pickleshare-0.7.4 prompt-toolkit-1.0.15 ptyprocess-0.5.2 python-dateutil-2.6.1 pyzmq-16.0.2 scandir-1.6 setuptools-36.6.0 six-1.11.0 terminado-0.6 testpath-0.3.1 tornado-4.5.2 traitlets-4.3.2 wcwidth-0.1.7 webencodings-0.5.1 widgetsnbextension-3.0.5

$ ipython notebook

Data Visualization.ipynb

Steering wheel prediction

Statistics of steering wheel prediction as reported in the RAL paper are in the spreadsheet below. The smallest error for each recording is shown in boldface.

DDD20 steering angle prediction errors
		RMSE steering wheel angle (deg)
	recording	DVS+APS	stddev	DVS	stddev	APS	stddev
night	1	6.18	0.17	7.49	0.50	6.41	0.16
	2	5.82	0.16	4.50	0.16	6.34	0.09
	3	4.86	0.56	4.31	0.05	5.80	0.34
	4	3.49	0.67	2.57	0.81	3.17	0.73
	5	2.75	0.03	2.78	0.02	2.77	0.02
	6	2.94	0.26	8.36	1.25	3.81	0.20
	7	3.34	0.34	4.12	0.32	3.57	0.20
	8	2.87	0.09	5.41	0.43	3.26	0.13
	9	3.11	0.18	3.69	0.24	3.40	0.25
	10	10.18	1.54	9.96	0.19	12.18	0.55
	11	7.59	0.60	7.89	0.55	8.25	0.26
	12	3.27	0.05	3.69	0.10	3.43	0.29
	13	3.51	0.07	3.55	0.02	3.51	0.04
	14	7.82	0.53	7.56	0.57	9.17	0.35
	15	2.53	0.12	2.52	0.05	2.57	0.11
night avg		4.68	0.52	5.23	0.48	5.18	0.31
day	16	3.25	0.01	3.27	0.02	3.26	0.03
	17	9.88	0.84	9.59	0.48	11.33	0.05
	18	1.80	0.01	1.82	0.00	1.80	0.02
	19	8.61	0.33	9.80	0.78	10.67	0.23
	20	5.71	0.49	9.23	0.86	6.42	0.33
	21	13.03	1.47	14.14	0.33	15.60	0.70
	22	31.40	0.74	34.73	1.02	59.57	1.29
	23	3.76	0.09	3.72	0.04	3.99	0.11
	24	10.54	0.64	9.28	0.64	11.89	0.65
	25	12.63	2.73	9.85	0.54	13.37	0.98
	26	11.05	0.28	11.29	0.17	11.14	0.26
	27	2.11	0.11	4.26	0.13	2.33	0.13
	28	2.14	0.06	2.06	0.05	2.08	0.12
	29	1.33	0.01	1.32	0.02	1.31	0.02
	30	2.58	0.13	2.74	0.23	2.58	0.12
day avg		7.99	0.88	8.47	0.49	10.49	0.51
overall avg		6.34	0.73	6.85	0.48	7.83	0.42

Recording new data

In addition to software for playback and export of the DDD17 dataset files, we provide tools for recording new data. The recording framework can be modified / extended to record data from arbitrary sources by creating a new ‘interface’ class.

The following instructions focus on recording visual data from a DVS/DAVIS camera and vehicle diagnostic/control data from an OpenXC vehicle interface.

Requirements

Hardware

A DAVIS event camera with AER output
An OpenXC-compatible vehicle interface
A compatible vehicle - see this useful spreadsheet of cars and data types available
Tested on linux Ubuntu 16 LTS. It also runs in a virtualbox linux guest under windows, but graphics slows it down probably too much for recording.

Software

For a virgin Ubuntu 16 LTS system, the script setup.sh can help guide specific steps needed for installation for recording.

In addition to the software required for viewing / exporting (opencv, opencv python bindings, hdf5 libraries, h5py; see Getting the software), a working installation of cAER is required, as well as the openxc tools for readout.

Specifically,

libcaer needs to be installed and functional (see libcaer README for installation instructions)
cAER should be built with ENABLE_NETWORK_OUTPUT=1 (see cAER README for details and additional software dependencies)
OpenXC python tools can be installed through “pip install openxc” (note that libusb needs to be present on the system for OpenXC to work.)

Firmware

Firmware needs to be installed to the OpenXC interface for the specific vehicle, or the emulation firmware can be used for testing a setup. Vehicle firmware requires registration with Ford as developer at https://developer.ford.com/register/ . Then firmware is available at https://developer.ford.com/pages/openxc
Open firmware repo (only for testing, not for actual vehicles) is https://github.com/openxc/vi-firmware . Latest 7.2 Ford release is here. See http://openxcplatform.com/vehicle-interface/firmware.html
Extract the emulation firmware from zip file; it is vi-emulator-firmware-FORDBOARD-ctv7.2.0.bin
In case of the Ford reference interface, firmware is installed by holding interface in reset with paperclip in reset button hole while USB is plugged in. The interface appears as a flash drive. The firmware.bin file can be replaced by one specific for vehicle or test emulation.

Recording checklist

Is the correct car firmware loaded to interface (and not emulator used for debugging)?
In terminal, run caer-bin to start camera
In 2nd terminal, cd to target directory for recording files, and ensure it is writeable
In 2nd terminal, run record.py to start recording.
1. If it dies complaining that Ford VI USB interface is not present,
  1. is the car ignition turned on?
  2. Does some other process still have the interface open?
  3. Is the cable plugged in? Sometimes it is necessary to unplug both ends of interface, plug in USB to computer, then plug into interface to car.
Hit enter to start recording, enter to stop recording.
Check recording with view.py rec, where file is the la
test recorded HDF5 file with view.py

Usage record.py

python record.py

- records to a new file recXXX.hdf5

python record.py /media/sf_recordings

- records to a new file recXXX.hdf5 in the folder /media/sf_recordings

Run cAER (cd <caer>; ./caer-bin) to capture DVS visual data
From ddd20-utils, test interface with python interfaces/caer.py
Run python record.py. A preview of the captured data is displayed, this can be used to align the camera etc. Press enter to start writing to a file.
Terminate the recording with ctrl-c.
You might prefer to run “./record.py”, so that in the event of a crash it is not needed to “killall record.py”. You may need to “chmod +x record.py”
A single argument is allowed that specifies the directory to save hdf5 file to

Troubleshooting

You can test DAVIS with

python interfaces/caer.py,

and test OpenXC with
python interfaces/oxc.py

You can also test the interface using

openxc-dashboard.

This utility is installed with openxc.

If OpenXC interface appears in lsusb but cannot be opened, add udev rules:

Make a new file
/etc/udev/rules.d/98-fordvi.rules,
in this file put the line
SUBSYSTEM=="usb", ATTR{idVendor}=="1bc4", ATTR{idProduct}=="0001", MODE="666"

Then replug the interface.

On a virgin install, you may need to install rules for the DAVIS camera as well. See inilabs documentation for thesbe e rules.

Sensor alignment

Most files in the run2 directory contain the 'alignment device'.
Try rec1487333001.hdf5 or rec1487337800.hdf5, for instance (very dark, auto exposure didn't exist back then...)
You can skip to a brighter section by clicking the timeline window of the viewer (third window)

APS exposure control

Exposure control is done in interfaces/caer.py.ExposureCtl. Options should be checked to be correct for camera mounting, i.e. if camera is mounted upside down, then the options

exposure = ExposureCtl(cutoff_bot=80, cutoff_top=0) # set for upside down camera where sky will be at bottom

at line 311 of caer.py are appropriate

Detecting corrupted recordings

The utility h5check can be used to detect corrupted hdf5 files. (These can occur any time the HDF5 file is not properly closed). Once h5check is installed, then use this shell command to move corrupted files to a subfolder “corrupted”, first make the folder with “mkdir corrupted”, then
for i in rec*.hdf5; do h5check $i || mv $i corruupted/; done

H5check is not installed by default. To obtain it see https://support.hdfgroup.org/products/hdf5_tools/h5check.html

Useful aliases for recording

These aliases and environment variables can be modified and put in your .bashrc file

#DATA=/media/tobi/MYLINUXLIVE/data

#DATA='/media/tobi/F0E885B6E8857C1A/Users/Tobi Delbruck/Resilio Sync/DDD17-DavisDrivingDataset2017/fordfocus'

DATA='/mnt/F0E885B6E8857C1A/Users/Tobi Delbruck/Resilio Sync/DDD17-DavisDrivingDataset2017/fordfocus'

PATH="/home/tobi/setups/ddd20-utils:/home/tobi/bin:/home/tobi/jdk1.8.0_131/jre/bin:$PATH"

#PATH="/home/tobi/setups/ford:$PATH"

alias r="killall record.py;record.py \"$DATA\"" # starts a recording to the DATA folder

alias cdd='cd "$DATA"' # cd to recordings folder

alias cdc='cd ~/setups/ddd20-utils' # cd to code folder

bind TAB:menu-complete # use uparrow for history completion, allows c^

alias va='for i in rec*.hdf5; do view.py $i; done' # view all recordings in current folder

alias v='view.py' # alias for view

alias vl='view.py `ls -t rec* | head -1`' # view last recording in current folder

alias d='pushd'

alias u='popd'

Useful commands

Produce CSV file of recordings

find . -name rec* -printf '"%P","%Tc","%TD","%TT","%s",\n' > listing.csv