TRI harnesses skills of professional drifter for nonlinear autonomous vehicle control models

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The Toyota Research Institute (TRI) in the USA has demonstrated a new research milestone for autonomous driving, successfully programming a vehicle to autonomously drift around obstacles on a closed track.

According to TRI, its Nonlinear Model Predictive Control (NMPC) approach extends a vehicle’s operational domain to the very limits of its performance. The idea behind the research is to utilize controlled, autonomous drifting to avoid accidents by navigating sudden obstacles or hazardous road conditions like black ice.

“At TRI, our goal is to use advanced technologies that augment and amplify humans, not replace them,” said Avinash Balachandran, senior manager of TRI’s Human Centric Driving Research. “Through this project, we are expanding the region in which a car is controllable, with the goal of giving regular drivers the instinctual reflexes of a professional race car driver to be able to handle the most challenging emergencies and keep people safer on the road.”

TRI notes that while most crashes occur in mundane situations, in some extreme situations, drivers may need to make maneuvers that take their vehicle close to and, at times, beyond normal limits of handling.

One year ago, TRI and the Dynamic Design Lab at Stanford University set out to design a new level of active safety to help avoid crashes, enlisting the support of automotive performance specialist GReddy and professional drift racer Ken Gushi. By building skills comparable to an expert driver, the technology is intended to amplify and augment a regular driver’s ability to respond to dangerous and extreme situations.

“When faced with wet or slippery roads, professional drivers may choose to ‘drift’ the car through a turn, but most of us are not professional drivers,” said Jonathan Goh, TRI research scientist. “That’s why TRI is programming vehicles that can identify obstacles and autonomously drift around obstacles on a closed track.”

The software developed by TRI calculates a whole new trajectory every 20th of a second to balance the car as it goes around the track. By combining the vehicle dynamics and control design insights from drifting-specific approaches with the generalized framework of NMPC, it is possible to devise a control scheme that extends the vehicle’s operational domain beyond the point of tire saturation. This allows the vehicle to drive beyond the notions of traditional open loop stability to where the vehicle is skidding but still controllable due to closed loop driving control.

This approach was tested using a Toyota Supra specially customized for autonomous driving research, equipped with computer-controlled steering, throttle, clutch displacement, sequential transmission and individual wheel braking. Vehicle state information is obtained from a dual-antenna RTK-GNSS-aided INS system at a rate of 250Hz, and the NMPC controller runs on an x86 computer.

For the purposes of data collection with expert drivers in a controlled environment, the suspension, engine, transmission, chassis and safety systems (e.g., roll cage, fire suppression) have been modified to be similar to that used in Formula Drift with the experiments conducted at Thunderhill Raceway, California, on the two-mile ‘West’ track.

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Lawrence has been covering engineering subjects – with a focus on motorsport technology – since 2007 and has edited and contributed to a variety of international titles. Currently, he oversees Automotive Powertrain Technology and Professional Motorsport World magazines as editor.

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