Canadian Manufacturing

Audi working on driving technology that could ‘predict the future’

Automaker said technology could tell drivers when to leave for appointments, find parking spots



SAN FRANCISCO—German automaker Audi AG is working with a trio of California universities and another in Michigan to develop a form of intelligent driving technology that could predict traffic and learn a driver’s habits.

Aimed to make “chaotic urban commutes less stressful,” Audi is working with the research schools to develop its Audi Urban Intelligent Assist (AUIA) program, which combines a host of technology to reduce driver stress, travel time and the dangers associated with inner-city commuting.

The technology, being developed in conjunction with Audi’s Electronics Research Laboratory (ERL) and the University of Southern California (USC), University of California—San Diego, University of California—Berkeley and University of Michigan Transportation Research Institute, can tell motorists the optimal time to leave for work, meetings and appointments, directing them on the most efficient route.

The program is also exploring naturalistic navigation based on visual cues, driver awareness monitoring and signals on when it is safe to merge or change lanes on a congested highway, according to Audi.

The AUIA project is the latest in a series of research projects that Audi has formed with leading American universities to explore the frontiers of automotive technologies and electronics.

The project was recently on display in San Francisco.

“The real-life mobility challenges presented by the streets of San Francisco provide a perfect opportunity to demonstrate how Audi Urban Intelligent Assist technology can transform a vehicle from a stylish expression of one’s self into a useful tool that can revolutionize the way a person drives,” ERL senior software engineer Mario Tippelhofer said in a release.

“This project brings to life a connected car that essentially predicts the behaviour of its driver, analyzes current and future driving conditions and creates a safer and hopefully less stressful experience for the person behind the wheel.”

The San Francisco AUIA demonstration focused on two primary applications: Audi Centric Urban Navigation (ACUN) and Audi Urban Assistance (AUA).

Both functions utilize traffic information from multiple on- and off-board sources to predict how traffic flows throughout the day and combines this information with driver diagnostic data to generate an optimal route that Audi said is tailor-made to each driver.

Under the ACUN application, drivers are notified through their mobile devices how long it will take them to reach their destination before they leave.

The application also utilizes a parking function that combines the parking habits of a driver with the availability of nearby parking spots, parking structures and metred on-street parking to help identify and navigate to parking near the driver’s destination.

Predictive traffic, also included in ACUN, anticipates and analyzes real-time traffic patterns based on present and past traffic data, along with weather and event information.

A Naturalistic Guidance function uses landmarks to provide detailed instructions for easy navigation, according to Audi.

For example, the program would instruct a driver to, “Please turn left at the park,” when applicable, rather than the typical distance indicators.

ACUN also provides walking directions from a chosen parking spot to the destination of choice.

Using speed calculation and green LED indicators in the side mirrors, the AUA application alerts drivers when it is save to merge onto highways and roads.

Similar to existing lane departure warning systems already on the market, the application also features Lane Change Assist, which monitors a driver’s blind spots as well as fast-approaching vehicles and hazardous objects in an effort to make lane changes safer.

The system also analyses driver behaviour to predict when the driver intends to performing a lane change.

An Attention Guard function provides early detection of driver distraction through countermeasures that get the attention of a driver and bring their focus back on the road.

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