U.S. Department of Transportation Posts Hyperloop Framework

(TNS) — Proposed hyperloop transportation systems, which developers say can move pods with passengers or freight through low-pressure tubes at more than 500 miles an hour, have received a key endorsement: validation by the U.S. Department of Transportation.

Transportation Secretary Elaine Chao released a 22-page document Thursday called Pathways to the Future of Transportation that’s designed to encourage innovation and place new transportation concepts under a specific regulatory agency. The document was developed by the Non-Traditional and Emerging Transportation Technology Council that Ms. Chao appointed about 18 months ago. 

For hyperloop advocates, the important step was placing hyperloop proposals under the Federal Railroad Administration and making hyperloop projects eligible for federal grants to help fund projects.

“This is a turning point for the industry,” said Ryan Kelly, vice president of Virgin Hyperloop One, one of two developers proposing systems to link Pittsburgh with Chicago.

“It gives confidence to stakeholders that

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JunshengFu/vehicle-detection: Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).

Objective

A demo of Vehicle Detection System: a monocular camera is used for detecting vehicles.

(1) Highway Drive (with Lane Departure Warning) (Click to see the full video)

gif_demo1

(2) City Drive (Vehicle Detection only) (Click to see the full video)

gif_demo2


Code & Files

1. My project includes the following files


Others are the same as in the repository of Lane Departure Warning System:

  • calibration.py contains the script to calibrate camera and save the calibration results
  • lane.py contains the lane class
  • examples folder contains the sample images and videos

2. Dependencies & my environment

Anaconda is used for managing my dependencies.

  • You can use provided environment-gpu.yml to install the dependencies.
  • OpenCV3, Python3.5, tensorflow, CUDA8
  • OS: Ubuntu 16.04

3. How to run the code

(1) Download weights for YOLO

You can download the weight from here and save it to
the weights folder.

(2) If you want to run the demo,

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