Nav: Home

Improving the vision of self-driving vehicles

March 06, 2020

There may be a better way for autonomous vehicles to learn how to drive themselves: by watching humans. With the help of an improved sight-correcting system, self-driving cars could learn just by observing human operators complete the same task.

Researchers from Deakin University in Australia published their results in IEEE/CAA Journal of Automatica Sinica, a joint publication of the Institute of Electrical and Electronics Engineers (IEEE) and the Chinese Association of Automation.

The team implemented imitation learning, also called learning from demonstration. A human operator drives a vehicle outfitted with three cameras, observing the environment from the front and each side of the car. The data is then processed through a neural network -- a computer system based on how the brain's neurons interact to process information -- that allows the vehicles to make decisions based on what it learned from watching the human make similar decisions.

"The expectation of this process is to generate a model solely from the images taken by the cameras," said paper author Saeid Nahavandi, Alfred Deakin Professor, pro vice-chancellor, chair of engineering and director for the Institute for Intelligent Systems Research and Innovation at Deakin University. "The generated model is then expected to drive the car autonomously."

The processing system is specifically a convolutional neural network, which is mirrored on the brain's visual cortex. The network has an input layer, an output layer and any number of processing layers between them. The input translates visual information into dots, which are then continuously compared as more visual information comes in. By reducing the visual information, the network can quickly process changes in the environment: a shift of dots appearing ahead could indicate an obstacle in the road. This, combined with the knowledge gained from observing the human operator, means that the algorithm knows that a sudden obstacle in the road should trigger the vehicle to fully stop to avoid an accident.

"Having a reliable and robust vision is a mandatory requirement in autonomous vehicles, and convolutional neural networks are one of the most successful deep neural networks for image processing applications," Nahavandi said.

He noted a couple of drawbacks, however. One is that imitation learning speeds up the training process while reducing the amount of training data required to produce a good model. In contrast, convolutional neural networks require a significant amount of training data to find an optimal configuration of layers and filters, which can help organize data, produces a properly generated model capable of driving an autonomous vehicle.

"For example, we found that increasing the number of filters does not necessarily result in a better performance," Nahavandi said. "The optimal selection of parameters of the network and training procedure is still an open question that researchers are actively investigating worldwide." Next, the researchers plan to study more intelligent and efficient techniques, including genetic and evolutionary algorithms to obtain the optimum set of parameters to better produce a self-learning, self-driving vehicle.
-end-
Other contributors include Parham Kebria, Abbas Khosravi and Syed Moshfeq Salaken, all of whom are with the Institute for Intelligent Systems Research and Innovation at Deakin University in Australia.

Fulltext of the paper is available: http://www.ieee-jas.org/en/article/doi/10.1109/JAS.2019.1911825

IEEE/CAA Journal of Automatica Sinica aims to publish high-quality, high-interest, far-reaching research achievements globally, and provide an international forum for the presentation of original ideas and recent results related to all aspects of automation. Researchers (including globally highly cited scholars) from institutions all over the world, such as MIT, Yale University, Stanford University, University of Cambridge, Princeton University, select to share their research with a large audience through JAS.

IEEE/CAA Journal of Automatica Sinica is indexed in SCIE, EI, Scopus, etc. The latest CiteScore is 5.31, ranked among top 9% (22/232) in the category of "Control and Systems Engineering", and top 10% (27/269, 20/189) both in the categories of "Information System" and "Artificial Intelligence". JAS has been in the 1st quantile (Q1) in all three categories it belongs to.

Why publish with us:
  • Fast and high quality peer review;
  • Simple and effective online submission system;
  • Widest possible global dissemination of your research;
  • Indexed in SCIE, EI, IEEE, Scopus, Inspec.
JAS papers can be found at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6570654 or http://www.ieee-jas.org

Chinese Association of Automation

Related Visual Information Articles:

Why visual perception is a decision process
A popular theory in neuroscience called predictive coding proposes that the brain produces all the time expectations that are compared with incoming information.
Visual impairment among women and dementia risk
Whether visual impairment is a risk factor for dementia was the focus of this observational study that included 1,000 older women who are participants in the Women's Health Initiative studies.
VR is not suited to visual memory?!
Toyohashi university of technology researcher and a research team at Tokyo Denki University have found that virtual reality (VR) may interfere with visual memory.
Dartmouth study finds conscious visual perception occurs outside the visual system
A Dartmouth study finds that the conscious perception of visual location occurs in the frontal lobes of the brain, rather than in the visual system in the back of the brain.
People with autism show atypical brain activity when coordinating visual and motor information
The brain is organized differently in individuals with ASD in its function for basic sensorimotor behaviors, but these functions can differ between people with autism.
Learning to read boosts the visual brain
How does learning to read change our brain? Does reading take up brain space dedicated to seeing objects such as faces, tools or houses?
How brain rhythms organize our visual perception
Imagine that you are watching a crowded hang-gliding competition, keeping track of a red and orange glider's skillful movements.
Seeing it both ways: Visual perspective in memory
Think of a memory from your childhood. Are you seeing the memory through your own eyes, or can you see yourself, while viewing that child as if you were an observer?
Using visual imagery to find your true passions
You may think you know what you like -- how to spend your time or what profession to pursue.
VisiBlends, a new approach to disrupt visual messaging
To help non-professionals create visual blends for their news and PSAs, Columbia Engineering researchers have developed VisiBlends, a flexible, user-friendly platform that transforms the creative brainstorming activity into a search function, and enables a statistically higher output of visually blended images.
More Visual Information News and Visual Information Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Listen Again: Meditations on Loneliness
Original broadcast date: April 24, 2020. We're a social species now living in isolation. But loneliness was a problem well before this era of social distancing. This hour, TED speakers explore how we can live and make peace with loneliness. Guests on the show include author and illustrator Jonny Sun, psychologist Susan Pinker, architect Grace Kim, and writer Suleika Jaouad.
Now Playing: Science for the People

#565 The Great Wide Indoors
We're all spending a bit more time indoors this summer than we probably figured. But did you ever stop to think about why the places we live and work as designed the way they are? And how they could be designed better? We're talking with Emily Anthes about her new book "The Great Indoors: The Surprising Science of how Buildings Shape our Behavior, Health and Happiness".
Now Playing: Radiolab

The Third. A TED Talk.
Jad gives a TED talk about his life as a journalist and how Radiolab has evolved over the years. Here's how TED described it:How do you end a story? Host of Radiolab Jad Abumrad tells how his search for an answer led him home to the mountains of Tennessee, where he met an unexpected teacher: Dolly Parton.Jad Nicholas Abumrad is a Lebanese-American radio host, composer and producer. He is the founder of the syndicated public radio program Radiolab, which is broadcast on over 600 radio stations nationwide and is downloaded more than 120 million times a year as a podcast. He also created More Perfect, a podcast that tells the stories behind the Supreme Court's most famous decisions. And most recently, Dolly Parton's America, a nine-episode podcast exploring the life and times of the iconic country music star. Abumrad has received three Peabody Awards and was named a MacArthur Fellow in 2011.