BigSnarf blog

Infosec FTW

Monthly Archives: March 2017

Predicting deep into the future with segmentation

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https://arxiv.org/pdf/1703.07684.pdf

https://github.com/MarvinTeichmann/MultiNet

https://arxiv.org/pdf/1612.07695.pdf

https://github.com/e-lab/ENet-training

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Self driving car LIDAR and camera download

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Self Driving Car Operating System

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TACC Traffic-Aware-Cruise-Control

tracking_white_van.gif

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russian_sdc

ff_autonomouscars3_f

901_Framegrab_Camera_sensor-e1441246576585

Traffic-Aware Cruise Control uses a camera

Mounted on the windshield behind the interior rear view mirror and a radar sensor in the
center of the front grill to detect whether there is a vehicle in front of you in the same
lane. If the area in front of car is clear, Traffic-Aware Cruise Control is designed to
drive consistently at a set speed.
When a vehicle is detected, Traffic-Aware Cruise Control is designed to slow down the car if
needed to maintain a selected time-based distance from the vehicle in front, up to the
set speed. Traffic-Aware Cruise Control does not eliminate the need to watch the road in
front of you and to apply the brakes if needed.
Traffic-Aware Cruise Control makes it easy to maintain a consistent time-based distance
from a vehicle travelling in front of you in the same lane. Traffic-Aware Cruise Control is
primarily intended for driving on dry, straight roads, such as highways and freeways. It
should not be used on city streets.

GOTURN tracking

Ego-motion

Optical Flow experiments

Filtering noise with Kalman Filters

The workhorse of robotics is Kalman Filters

kalman2kalman1

http://greg.czerniak.info/guides/kalman1/

Training Neural Networks for classification using the Extended Kalman Filter: A comparative study (effen paywalled)

http://www.frc.ri.cmu.edu/~alonzo/pubs/reports/kalman_V2.pdf

https://github.com/iqans/opencv-python/blob/master/kalman.py

https://arxiv.org/abs/1703.02310

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