Armed with this technology , the car could identify which driving patterns could lead to more risky maneuvers and act in real time to alert or assist the driver to change course .
Along for the Ride
Breakthroughs in understanding driver behavior and intent are products of a truly interdisciplinary collaboration at UC San Diego . The Laboratory for Intelligent and Safe Automobiles ( LISA ) works closely with leading researchers in a variety of fields , such as psychology professor Harold Pashler ; cognitive science professors Ed Hutchins , Revelle ’ 71 , M . A . ’ 73 , Ph . D . ’ 78 , and Jim Hollan ; and Scott Makeig , Ph . D . ‘ 85 , director of UC San Diego ’ s Swartz Center for
Computational Neuroscience . Trivedi readily credits these interdisciplinary partnerships as a strong influence on the early thinking of LISA .
“ Our collaborators are pioneers in the field of distributed cognition ,” says Trivedi . “ With their help , we ’ ve developed a new machine-learning-based paradigm that enables us to observe and learn the patterns which are associated with drivers ’ intentions to do safe maneuvers , as well as their intentions to change or not change the course of the journey .”
OVER THE LAST 15 YEARS , LISA researchers have undertaken projects funded by carmakers including Nissan , Toyota , Mercedes , Volkswagen and Audi , as well as various federal and California-funded programs . The team has pioneered technologies to monitor and assess what ’ s happening both inside and outside cars on the road — Trivedi calls this the LiLo approach , or “ looking in , looking out .” The team conducts its experiments by driving a fleet of testbed vehicles equipped with computer processors , cameras , GPS systems and other sensors that record the movements of the vehicle , the areas immediately surrounding the vehicle , as well as the movement of the driver ’ s head , eyes , hands and feet .
Researchers then use the data to develop machine vision and deep learning algorithms that help a car learn the driver ’ s patterns — where the driver looks , how the driver steers , when the driver tends to stop , go , slow down or speed up — and then predict the driver ’ s intended maneuvers a few seconds before they happen . Onboard computer processors run this information and send a set of instructions to actuators on the steering wheel , accelerator and brakes . Armed with this technology , the car could identify which driving patterns could lead to more risky maneuvers and act in real time to alert or assist the driver to change course .
For example , LISA researchers are developing intelligent driver assistance systems that assess when it ’ s safe to merge , brake , change lanes , accelerate and decelerate . So if drivers take their eyes off the road and begin swerving , cars could momentarily take control of steering and braking to avoid obstacles and collisions . The car could also determine the best speed at which to merge into the designated lane , based on the distances and speeds of cars in surrounding traffic .
“ These vehicles will have to understand various factors ,” says Trivedi . “ For instance , when and how to engage humans in controlling the vehicle in case of an emergency , the readiness of an occupant to take control , the gestures and intentions of humans in the car and on the streets , and also how to safely and smoothly move around vehicles which are driven in the old-fashioned way .”
To consider a human at the wheel “ old-fashioned ” speaks volumes about the pace of innovation and the drive behind making mere possibilities a reality on the roadways . As vehicles speed ever toward automation , new research challenges will be continuously emerging , a prospect that excites Trivedi . “ Our goal is to better understand dangerous and critical situations ,” he says . “ Ultimately , that understanding will help us and others to design effective counter-measures in order to make driving safer for drivers , passengers , pedestrians … anyone who may be at risk in an automotive incident .”
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