山东扑克3豹子走势图:ADAM on Eve of Driver Monitor Advance
扑克王粤语迅雷下载 www.kchiy.tw PARIS — The human mind has a tendency to sometimes wander off, unbidden, elsewhere — to a place irrelevant to the immediate, often urgent, matter at hand — without the user of the mind necessarily noticing this detour.
Cognitive scientists have long known this phenomenon and have been studying it for decades. They call it “mind wandering.” They talk about people getting hit by a train of thought that mentally disengages them from an attention-demanding task like reading, a face-to-face meeting, or, most important of all, driving.
To understand the cognitive state of a human brain — or the mental status of a human driver at any given moment — is believed to be the next frontier for driver-monitoring systems (DMS).
Think of Level 1, Level 2, or Level 3 cars with partially automated driver assistance systems. Even when your hands are on the wheel (as Tesla reminds you to do), your head is erect, and your eyes are open and staring at the street ahead, your brain could be picking daisies in La La Land.
When this happens, how can a machine — your car — recognize your impaired condition? What actions should it be programmed to take?
To some people, the DMS is an unglamorous, old-school, “boring” technology. They even ask, “Why DMS, with Level 4 autonomous vehicles hitting the home stretch?” Such notions, however, couldn’t be further from the current state of the art, according to Colin Barnden, Semicast Research lead analyst.
Ever since Euro NCAP, a voluntary vehicle safety rating system, made the driver-monitoring system a primary safety standard by 2020 in Euro NCAP’s Roadmap, car OEMs and Tier Ones are mobilizing DMS in every Level 2 car by 2020, explained Barnden. He believes that “the moment automation comes into a vehicle,” regardless of autonomy level, “it’s time to introduce DMS.”
The market already has a host of DMS technologies from companies such as Seeing Machines (Canberra, Australia), Smart Eye AB (Gothenburg, Sweden), Affectiva (Boston) and FotoNation (San Jose, California).
An Israel-based startup called ADAM is poised to join the club, claiming that it is the first to add a “cognition” layer to traditional driver-monitoring systems. The startup is at the “proof-of-concept” stage for many of the features to be embedded into its adaptive driver attention management (ADAM) platform.
However, Seeing Machines, for example, has amassed voluminous data on drivers’ behavior through 18 years of “human factor” R&D activities. So Barnden is skeptical that ADAM is truly the first with a “cognitive layer.” But he acknowledged that leading DMS technologies will no longer be about measuring just one thing — such as measuring head position, eye gaze, or eyelid closure. Instead, “human factors” need to consider a variety of parameters.
In an exclusive phone interview with EE Times, Pickering explained that he decided to join ADAM after 20 years with JLR because it is the first company that he has found with a critical technology that he had long sought at his previous company.
In developing HMI for cars, critically important to a scientist/engineer is knowing “how best to measure the driver’s workload.” A carmaker could decide on the adoption of hand gestures, handwriting, or voice recognition for its man-machine interface. But in evaluating each, “we must be able to measure the driver’s manual workload as the driver tinkers with switches and knobs in a car and visual workload as the driver looks at the road ahead,” said Pickering. The third element that comes into play is the “cognitive workload” that a driver must bear. “But really, how could we measure that? I had not been able to figure it out until I met a team at ADAM in Israel.”
The team has examined “a complex set of parameters” including eye gaze, pupil dilation, eyelid closures, blink rate, and others to discern a driver’s cognitive level. To set a baseline for cognition level — which varies from one person to another — the team has used AI and developed algorithms. With this, ADAM can measure the deviation from a driver’s baseline cognition level on an individual basis.
While acknowledging that all of these algorithms are still in the lab, Pickering said, “When I first saw how they are measuring the cognitive workload, I was blown away.”
Next page: A machine-to-driver handover