Miscellaneous

[Administrator]

Connected cars versus self driving cars | AT&T Mobile World Congress

Published on Dec 18, 2017

Learn the difference between connected cars and autonomous (or self-driving) cars at the AT&T booth the Mobile World Congress Americas.

 

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The future of self-driving cars | 2017 Year in Review

Published on Dec 23, 2017

 

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Article "AI in 2018: Waiting for driverless cars to become a reality"

by Claudia Assis
December 30, 2017

 

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How autonomous cars will be regionalized CES 2018

Published on Jan 10, 2018

 

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Building the framework that will keep OEMs on track for self-driving cars CES 2018

Published on Jan 10, 2018

 

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The state of self-driving cars: 2018

Published on Jan 30, 2018

Autonomous cars are supposed to be right around the corner, right? Well, not exactly. Every year car companies flock to CES and the Detroit Auto Show to show off their cool self-driving car concepts. And while cars are undoubtedly getting smarter, and the technology getting better and better, the day that you’ll be able to buy a self-driving car, or even ride in one, is a lot further out than you probably think.

 

[Administrator]

Make your car smarter in 5 minutes

Published on Feb 8, 2018

You don’t need a super-smart car to drive intelligently. This license plate backup sensor uses ultrasonic detection to tell you when you’re close to something.

It goes right over your license plate and pushes visual, audio, and vibration cues to your phone, alerting you when an object is close to the bumper.

Installing it is super straightforward because there’s no wiring. The included security screws are unique to FenSens, so only FenSens owners have the screwdriver to take the license plate off.

Once your frame is installed, there’s a button you can put on your steering wheel to instantly launch the FenSens app on your phone with one click. If it’s a car that different people drive, you can program it so multiple users can be connected to a single FenSens.

Your car doesn’t need to have a backup camera to know what’s behind it. This simple hack makes your car a whole lot smarter.

 

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Why are self-driving cars taking so long?

Published on May 24, 2018

Self-driving cars have been “coming soon” for years. What’s so hard about making a machine that drives itself!?

 

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The future of transportation - in conversation with Uri Levine and Dave Waiser

Published on Jun 11, 2018

Anna Escher sits down with Uri Levine and Dave Waiser to talk about the future of transportation.

 

[Administrator]

Semi autonomous vehicles test in Australia

Published on Jun 11, 2018

The Australian Road Research Board (ARRB) - the National Transport Research Organisation - has been collaborating with ConnectEast in a project for VicRoads investigating the capabilities and the driver-vehicle and vehicle-infrastructure interactions of semi autonomous vehicles (semi-AVs). This project in its initial stage has involved trialling 14 different vehicles on Eastlink (a privately-owned tollway in suburban Melbourne), and qualitatively assessing the performance of the Advanced Safety Features in each vehicle.

 

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Article "Self-driving cars are headed toward an AI roadblock"
Skeptics say full autonomy could be farther away than the industry admits

by Russell Brandom
July 3, 2018

 

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The collapsible crash test robot car

Published on Sep 10, 2018

The Global Vehicle Target is the new standard for testing autonomous driving and crash test systems. To cameras and radar, it looks like a car: but if you hit it, it'll fly apart. So if your emergency braking doesn't quite work... well, this is what happens.

Thanks to everyone at Thatcham Research! You can find out more about them at https://www.thatcham.org

 

[Montanist17]

Airicist... u are amazing guy!
A big thanks to you for this links and videos!
______________________
Bump

 

[Administrator]

Ford's sweaty robutt

Published on Jan 8, 2019

For the sweat test, “Robutt” simulates a decade’s worth of car use in just three days as it sits, bounces and twists in the seat 7,500 times.

Based on the dimensions of a large man, the robotic bottom is heated to 36° C, and soaked with 450 millilitres of water.
Introduced in 2018 for Fiesta, the “Robutt” seat test is now being rolled out for all Ford vehicles in Europe

 

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Largest autonomous car parade - Guinness World Records

Published on Jan 14, 2019

 

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How machine learning helps identify potholes on Los Angeles roads

Published on Jan 16, 2019

The streets of Los Angeles are peppered with potholes. To help identify and track them, three students at Loyola Marymount University developed a model using TensorFlow, Google’s open-source machine learning platform.

 

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Helping autonomous vehicles to communicate with pedestrians

Published on Feb 6, 2019

Hand gestures, head nods and thumbs-up signals all help to ensure drivers, pedestrians and cyclists know what each other is doing. But how will self driving vehicles, with no human driver, communicate with those around them?

 

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CES 2019 Trend: Vehicle technology

Published on Feb 6, 2019

Vehicle technology at CES is larger than many stand-alone car shows, featuring the hottest cars and connected vehicles. Vehicle tech is growing at a rapid page, with a focus on self-driving cars and driver-assistance technology.

 

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MIT Self-Driving Cars: State of the Art (2019)

Published on Feb 1, 2019

Introductory lecture of the MIT Self-Driving Cars series (6.S094) with an overview of the autonomous vehicle industry in 2018 and looking forward to 2019, including Waymo, Tesla, Cruise, Ford, GM, and out-of-the-box ideas of boring tunnels, flying cars, connected vehicles, and more. This covers the state of the art in terms of industry developments and not the perception and planning algorithm development. The latter will be covered in detail in future lectures. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.

Lex Fridman

INFO:
Website: deeplearning.mit.edu
GitHub: github.com/lexfridman/mit-deep-learning
Slides: dropbox.com/s/7in6e07mqiynvqi/self_driving_cars_state_of_the_art.pdf
Playlist: MIT Self-Driving Cars

OUTLINE:
0:00 - Introduction
1:53 - 2018 in review
4:49 - Fatalities
8:29 - Taxi services
10:54 - Predictions
16:55 - Human-centered autonomy
19:42 - Levels of autonomy and proliferation strategies
24:48 - Out-of-the-box ideas
27:28 - Who will be first?
29:26 - Historical context
31:05 - Underlying beliefs of the industry and public
32:32 - Driving is hard
35:32 - Humans are amazing
37:10 - Humans and automation don't mix well?
41:55 - Two approaches: Lidar vs Vision
49:54 - In the meantime… data
52:49 - The road ahead

 

[Administrator]

Predicting pedestrian movement in 3d for driverless cars

Published on Feb 15, 2019

This research has immediate applications to driverless cars. Much of the machine learning used to bring autonomous technology to its current level has dealt with two dimensional images—still photos. A computer shown several million photos of a stop sign will eventually come to recognize stop signs in the real world and in real time.

But by utilizing video clips that run for several seconds, the U-M system can study the first half of the snippet to make its predictions, and then verify the accuracy with the second half.

“If a pedestrian is playing with their phone, you know they’re distracted,” said Ram Vasudevan, a U-M assistant professor of mechanical engineering. “Their pose and where they’re looking is telling you a lot about their level of attentiveness. It’s also telling you a lot about what they’re capable of doing next.”

The research was conducted out of the U-M Ford Center for Autonomous Vehicles (FCAV) by Xiaoxiao Du, a research engineer in FCAV, Matthew Johnson-Roberson, an associate professor of naval architecture and marine engineering, and Vasudevan.
https://fcav.engin.umich.edu

Read the paper: "Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction" in IEEE Robotics and Automation Letters, 2019: https://doi.org/10.1109/LRA.2019.2895266

This video was produced by the FCAV lab, which acknowledges one of its former research engineers, Charles Barto, for his help in making this video, and also thanks Wonhui Kim and the rest of FCAV lab members who helped providing the PedX dataset used in this video.

https://www.engin.umich.edu