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

Objects Detection and Tracking Using Points Cloud Reconstructed from Linear Stereo Vision
https://www.intechopen.com/books/current-advancements-in-stereo-vision/objects-detection-and-tracking-using-points-cloud-reconstructed-from-linear-stereo-vision

7. Conclusion
   A method for detecting and tracking objects using linear stereo vision is presented. After
   reconstructing 3D points from the matching edge points extracted from stereo linear images,
   a clustering algorithm based on a spectral analysis is proposed to extract clusters of points
   where each cluster represents an object of the observed scene. The tracking process is
   achieved using Kalman filter algorithm and nearest neighbour data association. A fusion
   strategy is also proposed to resolve the problem of multiple clusters that represent a same
   object. The proposed method is tested with real data in the context of objects detection and
   tracking in front of a vehicle. �

See more articles in this area:

https://scholar.google.ca/scholar?q=Objects+Detection+and+Tracking+Using+Points+Cloud+Reconstructed+from+Linear+Stereo+Vision

One-Shot Video Object Segmentation.

http://vision.ee.ethz.ch/~cvlsegmentation/osvos/

https://github.com/scaelles/OSVOS-TensorFlow

Use https://github.com/zdzhaoyong/GSLAM for VisionBasedACC.
Integrate in Google Colaboratory.

Collect YouTube, Vimeo etc videos links, with all kinds of driving, that are good for testing SDC algorithms especially in the area of LKAS, VisionBased ACC, FCW, Pedestrian avoidance, etc.
You can try a few algorithms here, for free in the cloud with GPU acceleration, more will come soon:

https://medium.com/@mslavescu/try-live-ssd-object-detection-mask-r-cnn-object-detection-and-instance-segmentation-sfmlearner-df62bdc97d52

Here is my list of public videos as a starting point:

https://www.youtube.com/playlist?list=PLUop7b1Q1uZkv5__d2yPZG1cAXcelata8

Add your contributions in the comments here on this issue, or as comments on this video:
https://youtu.be/Z3bxoi0ZJ_g

NoScope: 1000x Faster Deep Learning Queries over Video

https://dawn.cs.stanford.edu/2017/06/22/noscope/

Code:
https://github.com/stanford-futuredata/noscope

And test it on the videos here:
#19

Geometric Deep Learning | Michael Bronstein || Radcliffe Institute
https://youtu.be/ptcBmEHDWds

Hey,

I watched your video here: https://www.youtube.com/watch?v=_cEouvyJNMs

And it led me to your python notebook that contains ssd net for object detection. I want to try out the distance estimation thing which isn't there in the notebook. Can you please link me with the correct notebook if this one's wrong or something else?

Thanks

SurfNet: Generating 3D shape surfaces using deep residual networks

https://arxiv.org/abs/1703.04079

Here is a dataset collected from two (not perfectly aligned) PS3 Eye cameras:

20180209_1736 - 2 x PS3 Eye + Android Sensors + OBD II data
https://drive.google.com/open?id=1KBWIUBp5nZNDiNcKVJP1Tzqs_5ll5S4Y

Left camera (3 GB)
https://drive.google.com/open?id=1WAv2m-APu0_rjMptRfK2DxRdvu2eWlvc
Right camera (3 GB)
https://drive.google.com/open?id=1-LMnWK2L_CRQMMBhGu9575HHhST2HLHP

Video with processed sample data from left camera (20180209_1736/left/001%4d.jpg):
https://www.youtube.com/watch?v=_cEouvyJNMs

The dataset contains the images from left and right cameras, sensors data (GPS, Accelerometer, Gyro, Compass, Magnetometer from Android phone) collected with Android Sensors app and OBD II data collected with Torque Lite Android app.

The images needs to be aligned and synchronized (can be done by using the signal or break lights of the cars) also with the sensors and OBD II data (based on start/stop periods).

Both the alignment and synchronization should be done automatically, so a program to do this will be required, to ease the use of future datasets like this

Machine Learning Meets Geometry
https://youtu.be/pgomVDoEMrc

Hello, I executed your Jupyter notebook in Colaboratory, environment is Python3 and GPU enabled.
I got this error on cell [12]:

ModuleNotFoundError: No module named 'youtube_dl'

To solve it please add before cell [12]:

os.environ['PAFY_BACKEND'] = 'internal'

Project code:

Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight
https://github.com/KumarRobotics/msckf_vio

Amazing demo video here:
https://youtu.be/jxfJFgzmNSw

Extended Object Tracking: Introduction, Overview and Applications

Karl Granstrom, Marcus Baum, Stephan Reuter

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.

https://arxiv.org/abs/1604.00970

Mask R-CNN in TensorFlow.

This repo attempts to reproduce this amazing work by Kaiming He et al. :

https://arxiv.org/abs/1703.06870

https://github.com/CharlesShang/FastMaskRCNN

Mobileye YouTube video

• https://youtu.be/lU4w0o-caFM
• https://youtu.be/H_wMyUEeIzQ

Try to reproduce this behavior in Vision Based ACC project.

Collect test videos from IIHS and other ADAS testing organizations, and also contribute with your own tests.

Provide in depth analysis of each video and situation.

See these kinds of articles, Mobileye specific:

https://scholar.google.ca/scholar?q=mobileye+vision+based+acc+paper

BoxCars: Improving Fine-Grained Recognition of Vehicles using 3D Bounding Boxes in Traffic Surveillance.

https://arxiv.org/abs/1703.00686