September 19, 2021
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Rekor Systems Announces Selection of Waycare Technologies by Louisiana Department of Transportation and Development for Pilot Program to Reduce State Traffic Congestion and Crashes
California homeless camp fire damages 2 bridges, disrupts public transportation
Austin finishes half of its bicycle network, expects to complete entire 400-mile system by 2025
The Top 10 Automotive Concepts that automotive enthusiasts will be itching to see on the road!
Oregon Transportation Commission, wary of I-5 Rose Quarter project’s growing price tag, grants conditional approval
Woman dies after being hit by car in North Windham Friday night
Silk-FAW Continues To Poach Italy’s Automotive Talent, As Lamborghini’s Katia Bassi Joins As Managing Director
Transportation Department cracks down on airlines withholding refunds for canceled flights
Bear gets trapped in car, destroys interior
Cycling apparel company adding full-service bike repair to visitor center
Latest Post
Rekor Systems Announces Selection of Waycare Technologies by Louisiana Department of Transportation and Development for Pilot Program to Reduce State Traffic Congestion and Crashes California homeless camp fire damages 2 bridges, disrupts public transportation Austin finishes half of its bicycle network, expects to complete entire 400-mile system by 2025 The Top 10 Automotive Concepts that automotive enthusiasts will be itching to see on the road! Oregon Transportation Commission, wary of I-5 Rose Quarter project’s growing price tag, grants conditional approval Woman dies after being hit by car in North Windham Friday night Silk-FAW Continues To Poach Italy’s Automotive Talent, As Lamborghini’s Katia Bassi Joins As Managing Director Transportation Department cracks down on airlines withholding refunds for canceled flights Bear gets trapped in car, destroys interior Cycling apparel company adding full-service bike repair to visitor center

Charleston’s Ashley River bicycle-pedestrian bridge project making strides | News

It was three days after a bicyclist died in a late-night crash with a Honda Accord on Charleston’s Ashley River Bridge.

Mayor John Tecklenburg and other city officials gathered on July 19 for a virtual meeting of the city’s Traffic and Transportation Committee to discuss a yearslong project that would have saved that bicyclist’s life.

“That’s the reason we’re doing this, to have safe passage back and forth between the peninsula and West Ashley,” Tecklenburg said. “Hopefully, when this bridge is completed, an incident like that just wouldn’t happen again.”

Chad Johnson, a 23-year-old from Texas was riding across the bridge around 11:50 p.m. July 16 when the crash claimed his life. He died at the scene and police continue to investigate the circumstances surrounding his death. 

Two drawbridges cross the Ashley River where Johnson died. They provide critical connections from downtown Charleston to the bustling suburbs of West Ashley. Each day, thousands of cars and trucks rumble their way across the U.S. Highway 17 spans.

But critics and transportation advocates have long argued the bridges were never designed with pedestrians and bicyclists in mind. A slim sidewalk, barely raised from the roadway and unguarded by any rail, fence or other barrier is all that separates them from injury and death. 

Advocates had pushed off and on for safe passage across the Ashley for almost a century, but efforts fell short time and time again until November 2019 when the city learned federal transportation officials had awarded an $18.1 million grant for a stand-alone bicycle and pedestrian bridge now known as the Ashley River Crossing. 

Despite the challenges and delays brought by the coronavirus pandemic, officials like Jason Kronsberg, the city’s parks director, said the staff has never stopped pushing the effort forward. 

They have been working with HDR, the city’s design-build support consultant, and with federal and state partners on environmental impact studies, aerial mapping and traffic studies, Kronsberg said during the committee meeting.

“Lots of stuff’s been going on behind the scenes where nobody’s seeing a lot, but (there’s) lots of work happening,” he said. 

The city aims to award a design-build contract by November 2022, have the final design complete in September 2023 and finish construction by late June 2025, Kronsberg said. 

The estimated price tag of the project is about $22 million.

For Katie Zimmerman, executive director of Charleston Moves, a nonprofit that’s long advocated for the bridge, seeing city officials committed to the project is helping to ease the frustrations of what’s proving to be a long, arduous process. 

Why is it so difficult to get a bike lane across the Ashley River?

And Zimmerman said she’s been trying to convey that message to other frustrated Charlestonians. 

“Because the majority of the funding is federal dollars, that adds a whole new layer of requirements,” she said. “There is no slow movement. It’s really all about the list of things that the city staff has to do in order to legally comply and follow all the federal requirements.”

Like Tecklenburg and other officials, Zimmerman points to


Brent Spence Bridge project 50% complete, transportation cabinet says

CINCINNATI (WXIX) – Kentucky Transportation Cabinet officials say the Brent Spence Bridge project is now 50% complete.

On March 1, the KYTC began the project to clean and paint the bridge.

“This project is a routine maintenance project that ensures the bridge is safe and sturdy for many years to come,” Branch Manager of Kentucky Transportation Cabinet Cory Wilson said.

Wilson also said tarps have been removed from the center span of the bridge revealing progress contractors have made.

The $36 million project is slated to wrap up in November, weather permitting, according to Wilson.

Traveling northbound, the two right (easternmost) lanes of the bridge are open; the two left (westernmost) lanes are closed.

Traveling southbound, the two right (westernmost) lanes are open; the two left (easternmost) lanes are closed.

The following ramps are still shut down:


  • The ramp to I-71 southbound from Fort Washington Way
  • The ramp to I-71 southbound from Third St.


  • The ramp to I-71/75 northbound from Fourth St.

Drivers are encouraged to plan their drive and use alternate routes to get around the bridge.

See a spelling or grammar error in our story? Please include the title when you click here to report it.

Copyright 2021 WXIX. All rights reserved.


Apple hires former BMW executive for its rebooted car project

Apple Inc. has hired Ulrich Kranz, a former senior executive at BMW AG’s electric car division, to help lead its own vehicle efforts, according to people familiar with the situation.

The technology giant hired Kranz in recent weeks, about a month after he stepped down as CEO of Canoo Inc., a developer of self-driving electric vehicles. Before co-founding Canoo, Kranz was senior vice president of the group that developed the i3 and i8 cars at BMW, where he worked for 30 years.

Kranz is one of Apple’s most significant automotive hires, a clear sign that the iPhone maker is determined to build a self-driving electric car to rival Tesla Inc. and other carmakers. Kranz will report to Doug Field, who led development of Tesla’s mass-market Model 3 and now runs Apple’s car project, said the people, who requested anonymity to discuss a private matter.

Apple has become the world’s most valuable company, with a market capitalization of more than $2 trillion, by selling iPhones, iPads, Apple Watches, Macs and services. With investors and customers clamoring for new products, the company has targeted cars and augmented-reality headsets. An Apple spokesperson confirmed Kranz’s hiring.

Apple began developing a vehicle in 2014 but shelved the effort around 2016 to focus on an autonomous platform it could sell to other companies or eventually use itself. Along the way, Apple poached several Tesla executives, who now help head up drive-train engineering, self-driving software and interiors and exteriors.

Last year, Apple gave oversight of the operation to John Giannandrea, senior vice president of machine learning and artificial intelligence and Field’s boss. Several months ago, Apple rebooted its efforts to develop a full-fledged electric car, but development remains in the early stages, so a launch is likely at least five years away.

Before hiring Kranz, Apple lost some key auto executives. Benjamin Lyon, Jaime Waydo and Dave Scott, who worked on engineering, safety systems and robotics, respectively, all departed in recent months. It’s unclear why the three left.

Following successful stints at BMW’s Mini division and teams working on sports cars and SUVs, Kranz was asked to run Project I, a battery-powered vehicle skunkworks started in 2008. It yielded the all-electric i3 compact and the plug-in hybrid i8 sports car. The former was panned by design critics, and production was very limited on the latter.

Kranz left BMW in 2016 and soon became chief technology officer at Faraday Future, an electric vehicle startup based in Los Angeles. He stayed only three months, before co-founding Canoo. Both firms have struggled with their technology and ability to produce vehicles, while Canoo reportedly discussed selling itself to Apple and other companies.

Canoo went public in December after a reverse merger with a special purpose acquisition company, or SPAC, called Hennessy Capital Acquisition Corp. Canoo last month said it was being investigated by the U.S. Securities and Exchange Commission, becoming the third clean-energy auto startup to disclose a federal probe in the past year. Canoo plans to debut a minivan for


Bicycle Generator Project | Clean Energy Research and Education

Wind for Schools worked with several teachers who expressed interest in using bicycle generators to teach their students some fundamental concepts of energy and basic mechanical, engineering, and electrical principles. With this project we worked with K-12 and college students to organize hands-on design and construction of bike generators. We then used the bike generators in the classroom for fun demonstrations which increased students’ understanding and awareness of energy topics.

History of the project

In 2010, Jeff Hines, a local Flagstaff teacher who also served as the first WindSenator in Arizona, inspired us to pursue bicycle generators for use in K-12 classrooms. Shortly after, we learned of an NAU student, Matthew Petney, who had built a double-bike generator, which included a battery for energy storage and an inverter and outlet so normal 120-volt devices could be plugged into it. We purchased the system from Matt and shared it with several interested teachers and classes as an educational tool. Matt joined our team in fall 2011 to provide more technical guidance to our staff and our teacher partners in building bike generators, bike blenders, and more.

In fall 2011 and spring 2012, Marilla Lamb and Matthew Petney visited two of our partner schools (Flagstaff Junior Academy and Orme School) to build bike blenders and a bike generator with middle and high school students. The students were presented with the design challenge, as well as tools and materials, and worked with our staff to design and build the bikes. These bikes were used at several school events, and in the classroom the following year as a teaching tool.

In 2011, Marilla Lamb wrote a grant to NAU’s Green Fund to fund a bicycle-powered charging station (The Eco-Pedaler), complete with energy meters so students can see the energy they produce and the energy they use, and with transparent coverings so all components are visible. The project was funded and a team of students designed and built the bike during 2012. The completed charging station can be seen in NAU’s engineering building. Now, a team of senior electrical and mechanical engineering students are working on the second iteration of the charging station, which is also funded by NAU’s Green Fund to improve its usability and versatility.

Wind for Schools was awarded funding from the APS Leadership Grant program in 2012, and obtained nearly $5,000 to work with several teachers in Arizona at some of our partner schools to build bicycle generators either in their science classes or with their science clubs. Our team built these bike generators with students at Mount Elden Middle School, Coconino High School, STAR School, Williams High School, and Northland Preparatory Academy in Spring 2013. Several energy lessons accompany the bicycle generators that we built and worked with in K-12 classrooms.

Using the bike generator in your classroom

The bike generator is a great tool for explaining difficult concepts like energy, power, electricity, and energy conversions. When students use the bike generator, they get a physical, hands-on understanding of these


GitHub – balancap/SDC-Vehicle-Detection: Udacity Project

The goad of this project is to implement a robust pipeline capable of detecting moving vehicles in real-time. Even though the project was designed for using classic Computer Vision techniques, namely HOG features and SVM classifier, in agreement the course organizers, I decided like a few other students to go for a deep learning approach.

Several important papers on object detection using deep convolutional networks have been published the last few years. More specifically, Faster R-CNN, YOLO and Single Shot MultiBox Detector are the present state-of-the-art in using CNN for real-time object detection.

Even though there are a few differences between the three previous approaches, they share the same general pipeline. Namely, the detection network is designed based on the following rules:

  • Use a deep convolutional network trained on ImageNet as a multi-scale source of features. Typically, VGG, ResNet or Inception;
  • Provide a collection of pre-defined anchors boxes tiling the image at different positions and scales. They serve the same purpose as the sliding window approach in classic CV detection algorithms;
  • For every anchor box, the modified CNN provides a probability for every class of object (and a no detection probability), and offsets (x, y, width and height) between the detected box and the associated anchor box.
  • The detection output of the network is post-processed using a Non-Maximum Selection algorithm, in order to remove overlapping boxes.

For this project, I decided to implement the SSD detector, as the later provides a good compromise between accuracy and speed (note that the last YOLOv2 article describes in fact a SSD-like network).

The author of the original SSD research paper had implemented SSD using the framework Caffe. As I could not find any satisfying TensorFlow implementation of the former, I decided to write my own from scratch. This task was more time-consuming than I had originally thought, but also allowed me to learn how to properly write a large TensorFlow pipeline, from TFRecords to TensorBoard! I left my pure SSD port in a different GitHub repository, and modified it for this vehicle detection project.

SSD architecture

As previously outlined, the SSD network used the concept of anchor boxes for object detection. The image below illustrates the concept: at several scales are pre-defined boxes with different sizes and ratios. The goal of SSD convolutional network is, for each of these anchor boxes, to detect if there is an object inside this box (or closely), and compute the offset between the object bounding box and the fixed anchor box.

In the case of SSD network, we use VGG as a based architecture: it provides high quality features at different scales, the former being then used as inputs for multibox modules in charge of computing the object type and coordinates for each anchor boxes. The architecture of the network we use is illustrated in the following TensorBoard graph. It follows the original SSD paper:

  • Convolutional Blocks 1 to 7 are exactly VGG modules. Hence, these weights can be imported from VGG weights, speeding massively training time;

KSU Physics Education Bike Project

KSU Physics Education Bike Project

Scientific and Cultural Aspects of the Bicycle:

An International Pedagogical Project


This project is a multi-national effort to collaborate on the adaptation
and creation of pedagogical materials.  The bicycle, a highly developed
yet simple device, is the focus of this effort.  Students and faculty
are using materials developed in a variety of countries and creating new
materials using contemporary multimedia.  This effort began almost
15 years ago when Robert Fuller and Dean Zollman created the videodisc
Transformations featuring the Bicycle
at about the same time that the
PLON Project in The Netherlands developed the teaching module Traffic
and the British Open University developed a course on Materials and Structures
which featured the bicycle.  These efforts were independent of each
other.  Since that time we have worked to combine instructional materials
from these and other countries.


Web Site at the Unversity of Amsterdam

Contents of KSU Bicycle Project Web Site

•  Description
and Application of 2000-2001 International Exchange Program

•  International
Study & Exchange Program

    European Community. and United States students enrolled in one of the
    partner institutions will become part of an international team of students
    who will investigate various scientific and cultural aspects of the bicycle,
    and create multimedia instructional materials about their activities. The
    students will become part of a three-year effort that will link international
    students by computer and bring them together periodically to work face-to-face.

• Workshops
on the Bicycle in Science, Technology and Culture, 1995

    These workshop, held in Great Britain and The Netherlands, brought
    together science and technology educators  and multimedia experts
    from the U.S., Australia, and several European countries.  Together
    they developed plans for pedagogical, multimedia materials for teaching
    about the bicycle.  This effort led to the International Study and
    Exchange Program.  U.S. participation in these workshops was supported
    by the National Science Foundation.

• International
Conference on the Bicycle in Science Pedagogy

    This conference, held in Lincoln, Nebraska, was jointly hosted by the
    University of Nebraska – Lincoln and Kansas State University.  Multimedia
    specialists, researchers on the science and technology of the bicycle and
    physics educators worked to gather to lay the basic ground work for a series
    of lessons on the science and technology of the bicycle and their cultural
    adaptations in different cultures.  The conference was supported by
    the Association of Big 8  (now Big 12) Universities.

• Resources

• Bicycle

Principal Investigator at Kansas State University is Dean

The project has received funding from the Association of Big 8
(now Big 12) Universities, the U.S. National Science Foundation, the European
Commission, and the U.S. Department of Education.

This page last updated on February 19, 1999

Copyright © 1999 Physics Education Group, Kansas
State University

Source Article


udacity/self-driving-car: The Udacity open source self-driving car project

Self-Driving Car

We’re Building an Open Source Self-Driving Car

And we want your help!

At Udacity, we believe in democratizing education. How can we provide opportunity to everyone on the planet? We also believe in teaching really amazing and useful subject matter. When we decided to build the Self-Driving Car Nanodegree program, to teach the world to build autonomous vehicles, we instantly knew we had to tackle our own self-driving car too.

Together with Google Self-Driving Car founder and Udacity President Sebastian Thrun, we formed our core Self-Driving Car Team. One of the first decisions we made? Open source code, written by hundreds of students from across the globe!

You can read more about our plans for this project.


Here’s a list of the projects we’ve open sourced:

How to Contribute

Like any open source project, this code base will require a certain amount of thoughtfulness. However, when you add a 2-ton vehicle into the equation, we also need to make safety our absolute top priority, and pull requests just don’t cut it. To really optimize for safety, we’re breaking down the problem of making the car autonomous into Udacity Challenges.


Each challenge will contain awesome prizes (cash and others) for the most effective contributions, but more importantly, the challenge format enables us to benchmark the safety of the code before we ever think of running it in the car. We believe challenges to be the best medium for us to build a Level-4 autonomous vehicle, while at the same time offering our contributors a valuable and exciting learning experience.

You can find a current list of challenges, with lots of information, on the Udacity self-driving car page. This is the primary way to contribute to this open source self-driving car project.

Core Contributors


Open Source Base Software Support

Tier IV

Source Article


ahmetozlu/vehicle_counting_tensorflow: “MORE THAN VEHICLE COUNTING!” This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API.

This sample project focuses on “Vechicle Detection, Tracking and Counting” using TensorFlow Object Counting API.

The TensorFlow Object Counting API is used as a base for object counting on this project, more info can be found on this repo.

The developing is on progress! This sample project will be updated soon, the more talented traffic analyzer app will be available in this repo!

General Capabilities of This Sample Project

This sample project has more than just counting vehicles, here are the additional capabilities of it:

  • Detection and classification of the vehicles (car, truck, bicycle, motorcycle, bus)
  • Recognition of approximate vehicle color
  • Detection of vehicle direction of travel
  • Prediction the speed of the vehicle
  • Prediction of approximate vehicle size
  • The images of detected vehicles are cropped from video frame and they are saved as new images under “detected_vehicles” folder path
  • The program gives a .csv file as an output (traffic_measurement.csv) which includes “Vehicle Type/Size”, ” Vehicle Color”, ” Vehicle Movement Direction”, ” Vehicle Speed (km/h)” rows, after the end of the process for the source video file.


  • More powerful detection models will be shared.
  • Sample codes will be developed to process different types of input videos (for different types of road traffics such as two way lane road).
  • Code cleanup will be performed.
  • UI will be developed.

The input video can be accessible by this link.


System Architecture

  • Vehicle detection and classification have been developed using TensorFlow Object Detection API, see for more info.
  • Vehicle speed prediction has been developed using OpenCV via image pixel manipulation and calculation, see for more info.
  • Vehicle color prediction has been developed using OpenCV via K-Nearest Neighbors Machine Learning Classification Algorithm is Trained Color Histogram Features, see for more info.

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them.

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.


Source video is read frame by frame with OpenCV. Each frames is processed by “SSD with Mobilenet” model is developed on TensorFlow. This is a loop that continue working till reaching end of the video. The main pipeline of the tracker is given at the above Figure.


By default I use an “SSD with Mobilenet” model in this project. You can find more information about SSD in here. See the detection model zoo for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

The minimum vehicle detection threshold can be set in this line in terms of percentage. The default minimum vehicle detecion threshold is 0.5!

Project Demo

Demo video of the project is available on My YouTube Channel.


Docker setup with Nvidia GPU: Run the demo in


GitHub – udacity/CarND-Vehicle-Detection: Vehicle Detection Project

Udacity - Self-Driving Car NanoDegree

In this project, your goal is to write a software pipeline to detect vehicles in a video (start with the test_video.mp4 and later implement on full project_video.mp4), but the main output or product we want you to create is a detailed writeup of the project. Check out the writeup template for this project and use it as a starting point for creating your own writeup.

Creating a great writeup:

A great writeup should include the rubric points as well as your description of how you addressed each point. You should include a detailed description of the code used in each step (with line-number references and code snippets where necessary), and links to other supporting documents or external references. You should include images in your writeup to demonstrate how your code works with examples.

All that said, please be concise! We’re not looking for you to write a book here, just a brief description of how you passed each rubric point, and references to the relevant code :).

You can submit your writeup in markdown or use another method and submit a pdf instead.

The Project

The goals / steps of this project are the following:

  • Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
  • Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
  • Note: for those first two steps don’t forget to normalize your features and randomize a selection for training and testing.
  • Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
  • Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.
  • Estimate a bounding box for vehicles detected.

Here are links to the labeled data for vehicle and non-vehicle examples to train your classifier. These example images come from a combination of the GTI vehicle image database, the KITTI vision benchmark suite, and examples extracted from the project video itself. You are welcome and encouraged to take advantage of the recently released Udacity labeled dataset to augment your training data.

Some example images for testing your pipeline on single frames are located in the test_images folder. To help the reviewer examine your work, please save examples of the output from each stage of your pipeline in the folder called ouput_images, and include them in your writeup for the project by describing what each image shows. The video called project_video.mp4 is the video your pipeline should work well on.

As an optional challenge Once you have a working pipeline for vehicle detection, add in your lane-finding algorithm from the last project to do simultaneous lane-finding and vehicle detection!

If you’re feeling ambitious (also totally optional though), don’t stop there! We encourage you to go out and take video of


Boise Bicycle Project

BBP is considered an “essential” business under the Governor’s Stay at Home Order as a social service and transportation organization. Below are the essential services being offered.

Social Services

Free Bicycle Repair: If your bicycle is your only/primary source of transportation, BBP is providing up to 30 minutes repair services at no cost. This service is intended for those without the ability to pay. Knock on the door Wed-Saturday 11-5pm and we will get you rolling.

Nonprofit Bike Repair: Partnering nonprofits can drop off bicycles to be repaired for their clients. If you are a nonprofit and have someone/s who needs their bike fixed, please call 208-429-6520 or email Adult and teen referrals are receiving priority repair services.

Bikes for Nonprofits: Partnering nonprofits can request bicycles for their clients in urgent need. BBP will work hard to provide these bicycles within 1-5 days of request. If you are a nonprofit and have someone/s who needs a bike, please call 208-429-6520 or email

Voucher Bikes: If you are collecting unemployment and need a bicycle to help find work, please ask your unemployment officer if you qualify for a voucher to purchase a bicycle from BBP.

Mobile Bike Repair: BBP is providing free bicycle repair for kids at Community Centers and nonprofits in the Boise area. If you work at anyplace where children of low-income families are gathering, please email to request a mobile repair stop.

***Refugee/New American, people experiencing homelessness, formerly incarcerated, low-income populations are receiving priority consideration.

Shop Services (Transportation)

Quality Affordable Bikes: BBP is continuing to provide quality affordable bikes to the community at-large, but we are doing it online only. At this time, we cannot do test rides, but you can return it within 30-days for store credit or another bike. All bikes sales now come with free $25 gift certificate for used parts.

Curbside New Used Parts: Need new or used parts to keep you rolling? Call 208-429-6520 and we’ll pull together all of your requested items, you pay online, and have them waiting for you at the door.

Essential Online Purchases: BBP has growing selection of essential new items for commuting. Pay online and we’ll have them waiting at the door.

*Repair as service: Hopefully coming soon. Drop off your bike at BBP we’ll fix it/tune it up and get it back to you at a fair price.

Volunteer Opportunities

Adopt-a-bike: Give us a call 208-429-6520 or email and we’ll find a fun project for you to work on from the safety of your home. We can even help load them so you don’t have to get out of your car. These bicycles will either sold to help is pay for our staff or will be donated through one of our programs.

Virtual Volunteers: This is a time when in-person volunteering is not safe. This is also a time when we need to work together to help the vulnerable. One of the best ways you can help is by serving as