Analyse Uber SDC accident

[mslavescu]

Collect datasets from similar situations and analyse them to help everyone understand what the car should have seen and what could have been done to avoid the accident.

Help us obtain the dataset from Uber SDC involved in the accident, at least 3 min before and 1 min after the impact (this is a reply to the police tweet with the video from the accident):
https://twitter.com/GTARobotics/status/976628350331518976

A few initial pointers to accident info:

The Google Maps StreetView link where the accident happened:

642 North Mill Avenue, Tempe, Arizona, USA
https://goo.gl/maps/wTDdCvSzc522

Brad Templeton analysis of the accident:
https://twitter.com/GTARobotics/status/976726328488710150
https://twitter.com/bradtem/status/978013912359555072

Experts Break Down the Self-Driving Uber Crash
https://twitter.com/GTARobotics/status/978025535807934470?s=09

Experts view on the fact that LIDAR should have detected the person from far away:
https://twitter.com/GTARobotics/status/977764787328356352

This is the moment when we decide that human lives matter more than cars
https://www.curbed.com/transportation/2018/3/20/17142090/uber-fatal-crash-driverless-pedestrian-safety

Uber self-driving system should have spotted woman, experts say
http://www.cbc.ca/beta/news/world/uber-self-driving-accident-video-1.4587439

IIHS shows the Volvo XC90 with a range just under 250 feet (76 meters) with "low beams" on!
https://twitter.com/GTARobotics/status/977995274122682368

Help us get current companies that test SDC to provide datasets from their own cars in similar situations as the accident:
https://twitter.com/GTARobotics/status/977773180344512512

Lets also capture current SDC sensors configurations/specs in:
https://github.com/OSSDC/OSSDC-Hacking-Book/wiki

Join the discussions on OSSDC Slack at http://ossdc.org

[mslavescu]

Would be super if we could also get full dataset from different SDC prototypes (including from Udacity, Waterloo, MIT and other universities) driving in similar conditions to see if current sensor technology is enough (I assume it is) to detect a moving target (person with a bike) at 50m while driving at 60km/h, then notify the safety driver that an obstacle is in range (FCW), automatically switch to long beam range to allow long range cameras to see better and slow down while approaching the target to increase the chances to stop or avoid impact.

We should also try to see how recent ADAS system behave in those conditions, in Tesla, Volkswagen, etc.

If anyone has access to this kind of cars (SDC or advanced ADAS) please share your investigations and tests.

This is the minimum that should have happened in the accident scenario, would be good to see how cars (as many as possible) with this feature activated would have behaved:

Forward Collision Warning (FCW) Systems
http://brainonboard.ca/safety_features/active_safety_features_fcw.php

"However, if an obstacle like a pedestrian or vehicle approaches quickly from the side, for example, the FCW system will have less time to assess the situation and therefore a driver relying exclusively on this safety feature to provide a warning of a collision will have less time to react."

[mslavescu]

A nice SDC related dataset from a Velodyne HDL 64E S2 LIDAR:

Project page:
http://www.pges.fr/2rt3d/

LIDAR scans:
http://www.pges.fr/2rt3d/downloads.php

Videos:
https://m.youtube.com/channel/UCQgt3-fe79kJhrR-tuK0M5Q

Lets see how visible is a person (or similarly shaped object) at around 50m and beyond.

[mslavescu]

To make it easier for everyone to understand the accident scenario, would be good to produce videos like these from real and simulated LIDAR scan:

Virtual scan of a pedestrian with a bicycle
https://youtu.be/zauRVtAcXUc

[mgschwan]

The blog entry to this video is here: http://www.blensor.org/blog_entry_20180323.html
We can do a more sophisticated simulation if necessary. This one was just to show that the person can be easily picked up from the LIDAR data

[mslavescu]

See the comments on this video, looks like Volvo XC90 has the best illumination between the 3 cars tested:

Volvo XC90, MB GLC & Audi Q7, night drive

https://youtu.be/9UCz5tm0cKo

We'll need to find if Uber car had active/dynamic beam mode on, that should have improved a lot the illumination range.

[mslavescu]

@mgschwan would be possible to convert some of the scans from 2rt3d dataset from above, to view them in your tool?

Also would be good to have similar videos with simulations at 25m and 50m for easy comparison.

Please add a bounding box with dimensions around the person+bike so we get a better feel of the scale and compare easier the point cloud.

[mgschwan]

@mslavescu the data is generated by our sensor simulation software. It can export to PCD point cloud data and be imported into any software that can read PCD files.

The visualization was done with Blender itself, no specialized tool was used. The colored pointcloud was generated with pcl_viewer from the pointcloud library.

I can do a scan at 25m and 50m, no problem, but I was wondering if there is some more information about the scene to make it a bit closer to reality?

• Lane width
• Location of the accident to look it up in google streetview

[mslavescu]

This shows how bad is the video from Uber dash cam, here it is compared with another video in the same road spot, similar night conditions:
https://twitter.com/emilyabell/status/977064719419879424

https://video.twimg.com/tweet_video/DY868_BXUAAfxt6.mp4

[mslavescu]

@mgschwan here is a picture with the Uber crash location, I added the Google StreetView link to the issue description:

https://twitter.com/Rashomon2/status/975808319200796672

[mslavescu]

National Transportation Safety Board Office of Public Affairs (NTSB) current report (3/21/2018):

https://www.ntsb.gov/news/press-releases/Pages/NR20180320.aspx

Developments on Tuesday include:

Meeting with representatives of Uber, the National Highway Traffic Safety Administration and the Tempe Police Department
Beginning examination of the accident vehicle, a Volvo XC90
Beginning examination of the accident site
Viewing a copy of a video that captured the crash from an aftermarket dash camera mounted in the test vehicle
Gathering information about the technology on the test vehicle
Collecting information about the pedestrian and the safety driver
Beginning collection of any and all electronic data stored on the test vehicle or transmitted to Uber

[mslavescu]

Velodyne HDL 64E S2 dataset (contains 152 pedestrians and 8 bicycles):

http://www-personal.acfr.usyd.edu.au/a.quadros/objects4.html

This object dataset was collected from the robot Shrimp (left), which has a Velodyne LIDAR. It spins around at 20Hz to produce a dynamic 3D point cloud of the scene. The dataset consists of 631 object instances segmented from Velodyne scans of the Sydney CBD. A few instances are shown below.

[mslavescu]

VeloView

Performs real-time visualization and processing of live captured 3D LiDAR data from Velodyne's HDL sensors (HDL-64E, HDL-32E, VLP-32, VLP-16, Puck, Puck Lite, Puck HiRes). An introduction to features of VeloView is available as a video

https://www.paraview.org/Wiki/VeloView

Code and datasets:

https://github.com/Kitware/VeloView

https://midas3.kitware.com/midas/community/29